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09 Strain GageThis is one of the most commonly used sensor. It is used in many transducers. Its fundamental operating principle is fairly easy to understand and it will be the purpose of this lecture. A strain gage is essentially a thin wire that is wrapped on film of plastic. The strain gage is then mounted (glued) on the part for which the strain must be measured. Stress, StrainWhen a beam is under axial load, the axial stress, $\sigma_a$, is defined as:\begin{align*}\sigma_a = \frac{F}{A}\end{align*}with $F$ the axial load, and $A$ the cross sectional area of the beam under axial load.Under the load, the beam of length $L$ will extend by $dL$, giving rise to the definition of strain, $\epsilon_a$:\begin{align*}\epsilon_a = \frac{dL}{L}\end{align*}The beam will also contract laterally: the cross sectional area is reduced by $dA$. This results in a transverval strain $\epsilon_t$. The transversal and axial strains are related by the Poisson's ratio:\begin{align*}\nu = - \frac{\epsilon_t }{\epsilon_a}\end{align*}For a metal the Poission's ratio is typically $\nu = 0.3$, for an incompressible material, such as rubber (or water), $\nu = 0.5$.Within the elastic limit, the axial stress and axial strain are related through Hooke's law by the Young's modulus, $E$:\begin{align*}\sigma_a = E \epsilon_a\end{align*} Resistance of a wireThe electrical resistance of a wire $R$ is related to its physical properties (the electrical resistiviy, $\rho$ in $\Omega$/m) and its geometry: length $L$ and cross sectional area $A$.\begin{align*}R = \frac{\rho L}{A}\end{align*}Mathematically, the change in wire dimension will result inchange in its electrical resistance. This can be derived from first principle:\begin{align}\frac{dR}{R} = \frac{d\rho}{\rho} + \frac{dL}{L} - \frac{dA}{A}\end{align}If the wire has a square cross section, then:\begin{align*}A & = L'^2 \\\frac{dA}{A} & = \frac{d(L'^2)}{L'^2} = \frac{2L'dL'}{L'^2} = 2 \frac{dL'}{L'}\end{align*}We have related the change in cross sectional area to the transversal strain.\begin{align*}\epsilon_t = \frac{dL'}{L'}\end{align*}Using the Poisson's ratio, we can relate then relate the change in cross-sectional area ($dA/A$) to axial strain $\epsilon_a = dL/L$.\begin{align*}\epsilon_t &= - \nu \epsilon_a \\\frac{dL'}{L'} &= - \nu \frac{dL}{L} \; \text{or}\\\frac{dA}{A} & = 2\frac{dL'}{L'} = -2 \nu \frac{dL}{L}\end{align*}Finally we can substitute express $dA/A$ in eq. for $dR/R$ and relate change in resistance to change of wire geometry, remembering that for a metal $\nu =0.3$:\begin{align}\frac{dR}{R} & = \frac{d\rho}{\rho} + \frac{dL}{L} - \frac{dA}{A} \\& = \frac{d\rho}{\rho} + \frac{dL}{L} - (-2\nu \frac{dL}{L}) \\& = \frac{d\rho}{\rho} + 1.6 \frac{dL}{L} = \frac{d\rho}{\rho} + 1.6 \epsilon_a\end{align}It also happens that for most metals, the resistivity increases with axial strain. In general, one can then related the change in resistance to axial strain by defining the strain gage factor:\begin{align}S = 1.6 + \frac{d\rho}{\rho}\cdot \frac{1}{\epsilon_a}\end{align}and finally, we have:\begin{align*}\frac{dR}{R} = S \epsilon_a\end{align*}$S$ is materials dependent and is typically equal to 2.0 for most commercially availabe strain gages. It is dimensionless.Strain gages are made of thin wire that is wraped in several loops, effectively increasing the length of the wire and therefore the sensitivity of the sensor._Question:Explain why a longer wire is necessary to increase the sensitivity of the sensor_.Most commercially available strain gages have a nominal resistance (resistance under no load, $R_{ini}$) of 120 or 350 $\Omega$.Within the elastic regime, strain is typically within the range $10^{-6} - 10^{-3}$, in fact strain is expressed in unit of microstrain, with a 1 microstrain = $10^{-6}$. Therefore, changes in resistances will be of the same order. If one were to measure resistances, we will need a dynamic range of 120 dB, whih is typically very expensive. Instead, one uses the Wheatstone bridge to transform the change in resistance to a voltage, which is easier to measure and does not require such a large dynamic range. Wheatstone bridge:The output voltage is related to the difference in resistances in the bridge:\begin{align*}\frac{V_o}{V_s} = \frac{R_1R_3-R_2R_4}{(R_1+R_4)(R_2+R_3)}\end{align*}If the bridge is balanced, then $V_o = 0$, it implies: $R_1/R_2 = R_4/R_3$.In practice, finding a set of resistors that balances the bridge is challenging, and a potentiometer is used as one of the resistances to do minor adjustement to balance the bridge. If one did not do the adjustement (ie if we did not zero the bridge) then all the measurement will have an offset or bias that could be removed in a post-processing phase, as long as the bias stayed constant.If each resistance $R_i$ is made to vary slightly around its initial value, ie $R_i = R_{i,ini} + dR_i$. For simplicity, we will assume that the initial value of the four resistances are equal, ie $R_{1,ini} = R_{2,ini} = R_{3,ini} = R_{4,ini} = R_{ini}$. This implies that the bridge was initially balanced, then the output voltage would be:\begin{align*}\frac{V_o}{V_s} = \frac{1}{4} \left( \frac{dR_1}{R_{ini}} - \frac{dR_2}{R_{ini}} + \frac{dR_3}{R_{ini}} - \frac{dR_4}{R_{ini}} \right)\end{align*}Note here that the changes in $R_1$ and $R_3$ have a positive effect on $V_o$, while the changes in $R_2$ and $R_4$ have a negative effect on $V_o$. In practice, this means that is a beam is a in tension, then a strain gage mounted on the branch 1 or 3 of the Wheatstone bridge will produce a positive voltage, while a strain gage mounted on branch 2 or 4 will produce a negative voltage. One takes advantage of this to increase sensitivity to measure strain. Quarter bridgeOne uses only one quarter of the bridge, ie strain gages are only mounted on one branch of the bridge.\begin{align*}\frac{V_o}{V_s} = \pm \frac{1}{4} \epsilon_a S\end{align*}Sensitivity, $G$:\begin{align*}G = \frac{V_o}{\epsilon_a} = \pm \frac{1}{4}S V_s\end{align*} Half bridgeOne uses half of the bridge, ie strain gages are mounted on two branches of the bridge.\begin{align*}\frac{V_o}{V_s} = \pm \frac{1}{2} \epsilon_a S\end{align*} Full bridgeOne uses of the branches of the bridge, ie strain gages are mounted on each branch.\begin{align*}\frac{V_o}{V_s} = \pm \epsilon_a S\end{align*}Therefore, as we increase the order of bridge, the sensitivity of the instrument increases. However, one should be carefull how we mount the strain gages as to not cancel out their measurement. _Exercise_1- Wheatstone bridge> How important is it to know \& match the resistances of the resistors you employ to create your bridge?> How would you do that practically?> Assume $R_1=120\,\Omega$, $R_2=120\,\Omega$, $R_3=120\,\Omega$, $R_4=110\,\Omega$, $V_s=5.00\,\text{V}$. What is $V_\circ$? | Vs = 5.00
Vo = (120**2-120*110)/(230*240) * Vs
print('Vo = ',Vo, ' V')
# typical range in strain a strain gauge can measure
# 1 -1000 micro-Strain
AxialStrain = 1000*10**(-6) # axial strain
StrainGageFactor = 2
R_ini = 120 # Ohm
R_1 = R_ini+R_ini*StrainGageFactor*AxialStrain
print(R_1)
Vo = (120**2-120*(R_1))/((120+R_1)*240) * Vs
print('Vo = ', Vo, ' V') | 120.24
Vo = -0.002497502497502434 V
| BSD-3-Clause | Lectures/09_StrainGage.ipynb | eiriniflorou/GWU-MAE3120_2022 |
> How important is it to know \& match the resistances of the resistors you employ to create your bridge?> How would you do that practically?> Assume $R_1= R_2 =R_3=120\,\Omega$, $R_4=120.01\,\Omega$, $V_s=5.00\,\text{V}$. What is $V_\circ$? | Vs = 5.00
Vo = (120**2-120*120.01)/(240.01*240) * Vs
print(Vo) | -0.00010416232656978944
| BSD-3-Clause | Lectures/09_StrainGage.ipynb | eiriniflorou/GWU-MAE3120_2022 |
2- Strain gage 1:One measures the strain on a bridge steel beam. The modulus of elasticity is $E=190$ GPa. Only one strain gage is mounted on the bottom of the beam; the strain gage factor is $S=2.02$.> a) What kind of electronic circuit will you use? Draw a sketch of it.> b) Assume all your resistors including the unloaded strain gage are balanced and measure $120\,\Omega$, and that the strain gage is at location $R_2$. The supply voltage is $5.00\,\text{VDC}$. Will $V_\circ$ be positive or negative when a downward load is added? In practice, we cannot have all resistances = 120 $\Omega$. at zero load, the bridge will be unbalanced (show $V_o \neq 0$). How could we balance our bridge?Use a potentiometer to balance bridge, for the load cell, we ''zero'' the instrument.Other option to zero-out our instrument? Take data at zero-load, record the voltage, $V_{o,noload}$. Substract $V_{o,noload}$ to my data. > c) For a loading in which $V_\circ = -1.25\,\text{mV}$, calculate the strain $\epsilon_a$ in units of microstrain. \begin{align*}\frac{V_o}{V_s} & = - \frac{1}{4} \epsilon_a S\\\epsilon_a & = -\frac{4}{S} \frac{V_o}{V_s}\end{align*} | S = 2.02
Vo = -0.00125
Vs = 5
eps_a = -1*(4/S)*(Vo/Vs)
print(eps_a) | 0.0004950495049504951
| BSD-3-Clause | Lectures/09_StrainGage.ipynb | eiriniflorou/GWU-MAE3120_2022 |
Tabular learner> The function to immediately get a `Learner` ready to train for tabular data The main function you probably want to use in this module is `tabular_learner`. It will automatically create a `TabulaModel` suitable for your data and infer the irght loss function. See the [tabular tutorial](http://docs.fast.ai/tutorial.tabular) for an example of use in context. Main functions | #export
@log_args(but_as=Learner.__init__)
class TabularLearner(Learner):
"`Learner` for tabular data"
def predict(self, row):
tst_to = self.dls.valid_ds.new(pd.DataFrame(row).T)
tst_to.process()
tst_to.conts = tst_to.conts.astype(np.float32)
dl = self.dls.valid.new(tst_to)
inp,preds,_,dec_preds = self.get_preds(dl=dl, with_input=True, with_decoded=True)
i = getattr(self.dls, 'n_inp', -1)
b = (*tuplify(inp),*tuplify(dec_preds))
full_dec = self.dls.decode((*tuplify(inp),*tuplify(dec_preds)))
return full_dec,dec_preds[0],preds[0]
show_doc(TabularLearner, title_level=3) | _____no_output_____ | Apache-2.0 | nbs/43_tabular.learner.ipynb | NickVlasov/fastai |
It works exactly as a normal `Learner`, the only difference is that it implements a `predict` method specific to work on a row of data. | #export
@log_args(to_return=True, but_as=Learner.__init__)
@delegates(Learner.__init__)
def tabular_learner(dls, layers=None, emb_szs=None, config=None, n_out=None, y_range=None, **kwargs):
"Get a `Learner` using `dls`, with `metrics`, including a `TabularModel` created using the remaining params."
if config is None: config = tabular_config()
if layers is None: layers = [200,100]
to = dls.train_ds
emb_szs = get_emb_sz(dls.train_ds, {} if emb_szs is None else emb_szs)
if n_out is None: n_out = get_c(dls)
assert n_out, "`n_out` is not defined, and could not be infered from data, set `dls.c` or pass `n_out`"
if y_range is None and 'y_range' in config: y_range = config.pop('y_range')
model = TabularModel(emb_szs, len(dls.cont_names), n_out, layers, y_range=y_range, **config)
return TabularLearner(dls, model, **kwargs) | _____no_output_____ | Apache-2.0 | nbs/43_tabular.learner.ipynb | NickVlasov/fastai |
If your data was built with fastai, you probably won't need to pass anything to `emb_szs` unless you want to change the default of the library (produced by `get_emb_sz`), same for `n_out` which should be automatically inferred. `layers` will default to `[200,100]` and is passed to `TabularModel` along with the `config`.Use `tabular_config` to create a `config` and cusotmize the model used. There is just easy access to `y_range` because this argument is often used.All the other arguments are passed to `Learner`. | path = untar_data(URLs.ADULT_SAMPLE)
df = pd.read_csv(path/'adult.csv')
cat_names = ['workclass', 'education', 'marital-status', 'occupation', 'relationship', 'race']
cont_names = ['age', 'fnlwgt', 'education-num']
procs = [Categorify, FillMissing, Normalize]
dls = TabularDataLoaders.from_df(df, path, procs=procs, cat_names=cat_names, cont_names=cont_names,
y_names="salary", valid_idx=list(range(800,1000)), bs=64)
learn = tabular_learner(dls)
#hide
tst = learn.predict(df.iloc[0])
#hide
#test y_range is passed
learn = tabular_learner(dls, y_range=(0,32))
assert isinstance(learn.model.layers[-1], SigmoidRange)
test_eq(learn.model.layers[-1].low, 0)
test_eq(learn.model.layers[-1].high, 32)
learn = tabular_learner(dls, config = tabular_config(y_range=(0,32)))
assert isinstance(learn.model.layers[-1], SigmoidRange)
test_eq(learn.model.layers[-1].low, 0)
test_eq(learn.model.layers[-1].high, 32)
#export
@typedispatch
def show_results(x:Tabular, y:Tabular, samples, outs, ctxs=None, max_n=10, **kwargs):
df = x.all_cols[:max_n]
for n in x.y_names: df[n+'_pred'] = y[n][:max_n].values
display_df(df) | _____no_output_____ | Apache-2.0 | nbs/43_tabular.learner.ipynb | NickVlasov/fastai |
Export - | #hide
from nbdev.export import notebook2script
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| Apache-2.0 | nbs/43_tabular.learner.ipynb | NickVlasov/fastai |
Aerospike Connect for Spark - SparkML Prediction Model Tutorial Tested with Java 8, Spark 3.0.0, Python 3.7, and Aerospike Spark Connector 3.0.0 SummaryBuild a linear regression model to predict birth weight using Aerospike Database and Spark.Here are the features used:- gestation weeks- mother’s age- father’s age- mother’s weight gain during pregnancy- [Apgar score](https://en.wikipedia.org/wiki/Apgar_score)Aerospike is used to store the Natality dataset that is published by CDC. The table is accessed in Apache Spark using the Aerospike Spark Connector, and Spark ML is used to build and evaluate the model. The model can later be converted to PMML and deployed on your inference server for predictions. Prerequisites1. Load Aerospike server if not alrady available - docker run -d --name aerospike -p 3000:3000 -p 3001:3001 -p 3002:3002 -p 3003:3003 aerospike2. Feature key needs to be located in AS_FEATURE_KEY_PATH3. [Download the connector](https://www.aerospike.com/enterprise/download/connectors/aerospike-spark/3.0.0/) | #IP Address or DNS name for one host in your Aerospike cluster.
#A seed address for the Aerospike database cluster is required
AS_HOST ="127.0.0.1"
# Name of one of your namespaces. Type 'show namespaces' at the aql prompt if you are not sure
AS_NAMESPACE = "test"
AS_FEATURE_KEY_PATH = "/etc/aerospike/features.conf"
AEROSPIKE_SPARK_JAR_VERSION="3.0.0"
AS_PORT = 3000 # Usually 3000, but change here if not
AS_CONNECTION_STRING = AS_HOST + ":"+ str(AS_PORT)
#Locate the Spark installation - this'll use the SPARK_HOME environment variable
import findspark
findspark.init()
# Below will help you download the Spark Connector Jar if you haven't done so already.
import urllib
import os
def aerospike_spark_jar_download_url(version=AEROSPIKE_SPARK_JAR_VERSION):
DOWNLOAD_PREFIX="https://www.aerospike.com/enterprise/download/connectors/aerospike-spark/"
DOWNLOAD_SUFFIX="/artifact/jar"
AEROSPIKE_SPARK_JAR_DOWNLOAD_URL = DOWNLOAD_PREFIX+AEROSPIKE_SPARK_JAR_VERSION+DOWNLOAD_SUFFIX
return AEROSPIKE_SPARK_JAR_DOWNLOAD_URL
def download_aerospike_spark_jar(version=AEROSPIKE_SPARK_JAR_VERSION):
JAR_NAME="aerospike-spark-assembly-"+AEROSPIKE_SPARK_JAR_VERSION+".jar"
if(not(os.path.exists(JAR_NAME))) :
urllib.request.urlretrieve(aerospike_spark_jar_download_url(),JAR_NAME)
else :
print(JAR_NAME+" already downloaded")
return os.path.join(os.getcwd(),JAR_NAME)
AEROSPIKE_JAR_PATH=download_aerospike_spark_jar()
os.environ["PYSPARK_SUBMIT_ARGS"] = '--jars ' + AEROSPIKE_JAR_PATH + ' pyspark-shell'
import pyspark
from pyspark.context import SparkContext
from pyspark.sql.context import SQLContext
from pyspark.sql.session import SparkSession
from pyspark.ml.linalg import Vectors
from pyspark.ml.regression import LinearRegression
from pyspark.sql.types import StringType, StructField, StructType, ArrayType, IntegerType, MapType, LongType, DoubleType
#Get a spark session object and set required Aerospike configuration properties
sc = SparkContext.getOrCreate()
print("Spark Verison:", sc.version)
spark = SparkSession(sc)
sqlContext = SQLContext(sc)
spark.conf.set("aerospike.namespace",AS_NAMESPACE)
spark.conf.set("aerospike.seedhost",AS_CONNECTION_STRING)
spark.conf.set("aerospike.keyPath",AS_FEATURE_KEY_PATH ) | Spark Verison: 3.0.0
| MIT | notebooks/spark/other_notebooks/AerospikeSparkMLLinearRegression.ipynb | artanderson/interactive-notebooks |
Step 1: Load Data into a DataFrame | as_data=spark \
.read \
.format("aerospike") \
.option("aerospike.set", "natality").load()
as_data.show(5)
print("Inferred Schema along with Metadata.")
as_data.printSchema() | +-----+--------------------+---------+------------+-------+-------------+---------------+-------------+----------+----------+----------+
|__key| __digest| __expiry|__generation| __ttl| weight_pnd|weight_gain_pnd|gstation_week|apgar_5min|mother_age|father_age|
+-----+--------------------+---------+------------+-------+-------------+---------------+-------------+----------+----------+----------+
| null|[00 E0 68 A0 09 5...|354071840| 1|2367835| 6.9996768185| 99| 36| 99| 13| 15|
| null|[01 B0 1F 4D D6 9...|354071839| 1|2367834| 5.291094288| 18| 40| 9| 14| 99|
| null|[02 C0 93 23 F1 1...|354071837| 1|2367832| 6.8122838958| 24| 39| 9| 42| 36|
| null|[02 B0 C4 C7 3B F...|354071838| 1|2367833|7.67649596284| 99| 39| 99| 14| 99|
| null|[02 70 2A 45 E4 2...|354071843| 1|2367838| 7.8594796403| 40| 39| 8| 13| 99|
+-----+--------------------+---------+------------+-------+-------------+---------------+-------------+----------+----------+----------+
only showing top 5 rows
Inferred Schema along with Metadata.
root
|-- __key: string (nullable = true)
|-- __digest: binary (nullable = false)
|-- __expiry: integer (nullable = false)
|-- __generation: integer (nullable = false)
|-- __ttl: integer (nullable = false)
|-- weight_pnd: double (nullable = true)
|-- weight_gain_pnd: long (nullable = true)
|-- gstation_week: long (nullable = true)
|-- apgar_5min: long (nullable = true)
|-- mother_age: long (nullable = true)
|-- father_age: long (nullable = true)
| MIT | notebooks/spark/other_notebooks/AerospikeSparkMLLinearRegression.ipynb | artanderson/interactive-notebooks |
To speed up the load process at scale, use the [knobs](https://www.aerospike.com/docs/connect/processing/spark/performance.html) available in the Aerospike Spark Connector. For example, **spark.conf.set("aerospike.partition.factor", 15 )** will map 4096 Aerospike partitions to 32K Spark partitions. (Note: Please configure this carefully based on the available resources (CPU threads) in your system.) Step 2 - Prep data | # This Spark3.0 setting, if true, will turn on Adaptive Query Execution (AQE), which will make use of the
# runtime statistics to choose the most efficient query execution plan. It will speed up any joins that you
# plan to use for data prep step.
spark.conf.set("spark.sql.adaptive.enabled", 'true')
# Run a query in Spark SQL to ensure no NULL values exist.
as_data.createOrReplaceTempView("natality")
sql_query = """
SELECT *
from natality
where weight_pnd is not null
and mother_age is not null
and father_age is not null
and father_age < 80
and gstation_week is not null
and weight_gain_pnd < 90
and apgar_5min != "99"
and apgar_5min != "88"
"""
clean_data = spark.sql(sql_query)
#Drop the Aerospike metadata from the dataset because its not required.
#The metadata is added because we are inferring the schema as opposed to providing a strict schema
columns_to_drop = ['__key','__digest','__expiry','__generation','__ttl' ]
clean_data = clean_data.drop(*columns_to_drop)
# dropping null values
clean_data = clean_data.dropna()
clean_data.cache()
clean_data.show(5)
#Descriptive Analysis of the data
clean_data.describe().toPandas().transpose() | +------------------+---------------+-------------+----------+----------+----------+
| weight_pnd|weight_gain_pnd|gstation_week|apgar_5min|mother_age|father_age|
+------------------+---------------+-------------+----------+----------+----------+
| 7.5398093604| 38| 39| 9| 42| 41|
| 7.3634395508| 25| 37| 9| 14| 18|
| 7.06361087448| 26| 39| 9| 42| 28|
|6.1244416383599996| 20| 37| 9| 44| 41|
| 7.06361087448| 49| 38| 9| 14| 18|
+------------------+---------------+-------------+----------+----------+----------+
only showing top 5 rows
| MIT | notebooks/spark/other_notebooks/AerospikeSparkMLLinearRegression.ipynb | artanderson/interactive-notebooks |
Step 3 Visualize Data | import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import math
pdf = clean_data.toPandas()
#Histogram - Father Age
pdf[['father_age']].plot(kind='hist',bins=10,rwidth=0.8)
plt.xlabel('Fathers Age (years)',fontsize=12)
plt.legend(loc=None)
plt.style.use('seaborn-whitegrid')
plt.show()
'''
pdf[['mother_age']].plot(kind='hist',bins=10,rwidth=0.8)
plt.xlabel('Mothers Age (years)',fontsize=12)
plt.legend(loc=None)
plt.style.use('seaborn-whitegrid')
plt.show()
'''
pdf[['weight_pnd']].plot(kind='hist',bins=10,rwidth=0.8)
plt.xlabel('Babys Weight (Pounds)',fontsize=12)
plt.legend(loc=None)
plt.style.use('seaborn-whitegrid')
plt.show()
pdf[['gstation_week']].plot(kind='hist',bins=10,rwidth=0.8)
plt.xlabel('Gestation (Weeks)',fontsize=12)
plt.legend(loc=None)
plt.style.use('seaborn-whitegrid')
plt.show()
pdf[['weight_gain_pnd']].plot(kind='hist',bins=10,rwidth=0.8)
plt.xlabel('mother’s weight gain during pregnancy',fontsize=12)
plt.legend(loc=None)
plt.style.use('seaborn-whitegrid')
plt.show()
#Histogram - Apgar Score
print("Apgar Score: Scores of 7 and above are generally normal; 4 to 6, fairly low; and 3 and below are generally \
regarded as critically low and cause for immediate resuscitative efforts.")
pdf[['apgar_5min']].plot(kind='hist',bins=10,rwidth=0.8)
plt.xlabel('Apgar score',fontsize=12)
plt.legend(loc=None)
plt.style.use('seaborn-whitegrid')
plt.show() | _____no_output_____ | MIT | notebooks/spark/other_notebooks/AerospikeSparkMLLinearRegression.ipynb | artanderson/interactive-notebooks |
Step 4 - Create Model**Steps used for model creation:**1. Split cleaned data into Training and Test sets2. Vectorize features on which the model will be trained3. Create a linear regression model (Choose any ML algorithm that provides the best fit for the given dataset)4. Train model (Although not shown here, you could use K-fold cross-validation and Grid Search to choose the best hyper-parameters for the model)5. Evaluate model | # Define a function that collects the features of interest
# (mother_age, father_age, and gestation_weeks) into a vector.
# Package the vector in a tuple containing the label (`weight_pounds`) for that
# row.##
def vector_from_inputs(r):
return (r["weight_pnd"], Vectors.dense(float(r["mother_age"]),
float(r["father_age"]),
float(r["gstation_week"]),
float(r["weight_gain_pnd"]),
float(r["apgar_5min"])))
#Split that data 70% training and 30% Evaluation data
train, test = clean_data.randomSplit([0.7, 0.3])
#Check the shape of the data
train.show()
print((train.count(), len(train.columns)))
test.show()
print((test.count(), len(test.columns)))
# Create an input DataFrame for Spark ML using the above function.
training_data = train.rdd.map(vector_from_inputs).toDF(["label",
"features"])
# Construct a new LinearRegression object and fit the training data.
lr = LinearRegression(maxIter=5, regParam=0.2, solver="normal")
#Voila! your first model using Spark ML is trained
model = lr.fit(training_data)
# Print the model summary.
print("Coefficients:" + str(model.coefficients))
print("Intercept:" + str(model.intercept))
print("R^2:" + str(model.summary.r2))
model.summary.residuals.show() | Coefficients:[0.00858931617782676,0.0008477851947958541,0.27948866120791893,0.009329081045860402,0.18817058385589935]
Intercept:-5.893364345930709
R^2:0.3970187134779115
+--------------------+
| residuals|
+--------------------+
| -1.845934264937739|
| -2.2396120149639067|
| -0.7717836944756593|
| -0.6160804608336026|
| -0.6986641251138215|
| -0.672589930891391|
| -0.8699157049741881|
|-0.13870265354963962|
|-0.26366319351660383|
| -0.5260646593713352|
| 0.3191520988648042|
| 0.08956511232072462|
| 0.28423773834709554|
| 0.5367216316177004|
|-0.34304851596998454|
| 0.613435294490146|
| 1.3680838827256254|
| -1.887922569557201|
| -1.4788456210255978|
| -1.5035698497034602|
+--------------------+
only showing top 20 rows
| MIT | notebooks/spark/other_notebooks/AerospikeSparkMLLinearRegression.ipynb | artanderson/interactive-notebooks |
Evaluate Model | eval_data = test.rdd.map(vector_from_inputs).toDF(["label",
"features"])
eval_data.show()
evaluation_summary = model.evaluate(eval_data)
print("MAE:", evaluation_summary.meanAbsoluteError)
print("RMSE:", evaluation_summary.rootMeanSquaredError)
print("R-squared value:", evaluation_summary.r2) | +------------------+--------------------+
| label| features|
+------------------+--------------------+
| 3.62439958728|[42.0,37.0,35.0,5...|
| 5.3351867404|[43.0,48.0,38.0,6...|
| 6.8122838958|[42.0,36.0,39.0,2...|
| 6.9776305923|[46.0,42.0,39.0,2...|
| 7.06361087448|[14.0,18.0,38.0,4...|
| 7.3634395508|[14.0,18.0,37.0,2...|
| 7.4075320032|[45.0,45.0,38.0,1...|
| 7.68751907594|[42.0,49.0,38.0,2...|
| 3.09088091324|[43.0,46.0,32.0,4...|
| 5.62619692624|[44.0,50.0,39.0,2...|
|6.4992274837599995|[42.0,47.0,39.0,2...|
|6.5918216337999995|[42.0,38.0,35.0,6...|
| 6.686620406459999|[14.0,17.0,38.0,3...|
| 6.6910296517|[42.0,42.0,40.0,3...|
| 6.8122838958|[14.0,15.0,35.0,1...|
| 7.1870697412|[14.0,15.0,36.0,4...|
| 7.4075320032|[43.0,45.0,40.0,1...|
| 7.4736706818|[43.0,53.0,37.0,4...|
| 7.62578964258|[43.0,46.0,38.0,3...|
| 7.62578964258|[42.0,37.0,39.0,3...|
+------------------+--------------------+
only showing top 20 rows
MAE: 0.9094828902906563
RMSE: 1.1665322992147173
R-squared value: 0.378390902740944
| MIT | notebooks/spark/other_notebooks/AerospikeSparkMLLinearRegression.ipynb | artanderson/interactive-notebooks |
Step 5 - Batch Prediction | #eval_data contains the records (ideally production) that you'd like to use for the prediction
predictions = model.transform(eval_data)
predictions.show() | +------------------+--------------------+-----------------+
| label| features| prediction|
+------------------+--------------------+-----------------+
| 3.62439958728|[42.0,37.0,35.0,5...|6.440847435018738|
| 5.3351867404|[43.0,48.0,38.0,6...| 6.88674880594522|
| 6.8122838958|[42.0,36.0,39.0,2...|7.315398187463249|
| 6.9776305923|[46.0,42.0,39.0,2...|7.382829406480911|
| 7.06361087448|[14.0,18.0,38.0,4...|7.013375565916365|
| 7.3634395508|[14.0,18.0,37.0,2...|6.509988959607797|
| 7.4075320032|[45.0,45.0,38.0,1...|7.013333055266812|
| 7.68751907594|[42.0,49.0,38.0,2...|7.244430398689434|
| 3.09088091324|[43.0,46.0,32.0,4...|5.543968185959089|
| 5.62619692624|[44.0,50.0,39.0,2...|7.344445812546044|
|6.4992274837599995|[42.0,47.0,39.0,2...|7.287407500422561|
|6.5918216337999995|[42.0,38.0,35.0,6...| 6.56297327380972|
| 6.686620406459999|[14.0,17.0,38.0,3...|7.079420310981281|
| 6.6910296517|[42.0,42.0,40.0,3...|7.721251613436126|
| 6.8122838958|[14.0,15.0,35.0,1...|5.836519309057246|
| 7.1870697412|[14.0,15.0,36.0,4...|6.179722574647495|
| 7.4075320032|[43.0,45.0,40.0,1...|7.564460826372854|
| 7.4736706818|[43.0,53.0,37.0,4...|6.938016907316393|
| 7.62578964258|[43.0,46.0,38.0,3...| 6.96742600202968|
| 7.62578964258|[42.0,37.0,39.0,3...|7.456182188345951|
+------------------+--------------------+-----------------+
only showing top 20 rows
| MIT | notebooks/spark/other_notebooks/AerospikeSparkMLLinearRegression.ipynb | artanderson/interactive-notebooks |
Compare the labels and the predictions, they should ideally match up for an accurate model. Label is the actual weight of the baby and prediction is the predicated weight Saving the Predictions to Aerospike for ML Application's consumption | # Aerospike is a key/value database, hence a key is needed to store the predictions into the database. Hence we need
# to add the _id column to the predictions using SparkSQL
predictions.createOrReplaceTempView("predict_view")
sql_query = """
SELECT *, monotonically_increasing_id() as _id
from predict_view
"""
predict_df = spark.sql(sql_query)
predict_df.show()
print("#records:", predict_df.count())
# Now we are good to write the Predictions to Aerospike
predict_df \
.write \
.mode('overwrite') \
.format("aerospike") \
.option("aerospike.writeset", "predictions")\
.option("aerospike.updateByKey", "_id") \
.save() | _____no_output_____ | MIT | notebooks/spark/other_notebooks/AerospikeSparkMLLinearRegression.ipynb | artanderson/interactive-notebooks |
Concurrency with asyncio Thread vs. coroutine | # spinner_thread.py
import threading
import itertools
import time
import sys
class Signal:
go = True
def spin(msg, signal):
write, flush = sys.stdout.write, sys.stdout.flush
for char in itertools.cycle('|/-\\'):
status = char + ' ' + msg
write(status)
flush()
write('\x08' * len(status))
time.sleep(.1)
if not signal.go:
break
write(' ' * len(status) + '\x08' * len(status))
def slow_function():
time.sleep(3)
return 42
def supervisor():
signal = Signal()
spinner = threading.Thread(target=spin, args=('thinking!', signal))
print('spinner object:', spinner)
spinner.start()
result = slow_function()
signal.go = False
spinner.join()
return result
def main():
result = supervisor()
print('Answer:', result)
if __name__ == '__main__':
main()
# spinner_asyncio.py
import asyncio
import itertools
import sys
@asyncio.coroutine
def spin(msg):
write, flush = sys.stdout.write, sys.stdout.flush
for char in itertools.cycle('|/-\\'):
status = char + ' ' + msg
write(status)
flush()
write('\x08' * len(status))
try:
yield from asyncio.sleep(.1)
except asyncio.CancelledError:
break
write(' ' * len(status) + '\x08' * len(status))
@asyncio.coroutine
def slow_function():
yield from asyncio.sleep(3)
return 42
@asyncio.coroutine
def supervisor():
#Schedule the execution of a coroutine object:
#wrap it in a future. Return a Task object.
spinner = asyncio.ensure_future(spin('thinking!'))
print('spinner object:', spinner)
result = yield from slow_function()
spinner.cancel()
return result
def main():
loop = asyncio.get_event_loop()
result = loop.run_until_complete(supervisor())
loop.close()
print('Answer:', result)
if __name__ == '__main__':
main()
# flags_asyncio.py
import asyncio
import aiohttp
from flags import BASE_URL, save_flag, show, main
@asyncio.coroutine
def get_flag(cc):
url = '{}/{cc}/{cc}.gif'.format(BASE_URL, cc=cc.lower())
resp = yield from aiohttp.request('GET', url)
image = yield from resp.read()
return image
@asyncio.coroutine
def download_one(cc):
image = yield from get_flag(cc)
show(cc)
save_flag(image, cc.lower() + '.gif')
return cc
def download_many(cc_list):
loop = asyncio.get_event_loop()
to_do = [download_one(cc) for cc in sorted(cc_list)]
wait_coro = asyncio.wait(to_do)
res, _ = loop.run_until_complete(wait_coro)
loop.close()
return len(res)
if __name__ == '__main__':
main(download_many)
# flags2_asyncio.py
import asyncio
import collections
import aiohttp
from aiohttp import web
import tqdm
from flags2_common import HTTPStatus, save_flag, Result, main
DEFAULT_CONCUR_REQ = 5
MAX_CONCUR_REQ = 1000
class FetchError(Exception):
def __init__(self, country_code):
self.country_code = country_code
@asyncio.coroutine
def get_flag(base_url, cc):
url = '{}/{cc}/{cc}.gif'.format(BASE_URL, cc=cc.lower())
resp = yield from aiohttp.ClientSession().get(url)
if resp.status == 200:
image = yield from resp.read()
return image
elif resp.status == 404:
raise web.HTTPNotFound()
else:
raise aiohttp.HttpProcessingError(
code=resp.status, message=resp.reason, headers=resp.headers)
@asyncio.coroutine
def download_one(cc, base_url, semaphore, verbose):
try:
with (yield from semaphore):
image = yield from get_flag(base_url, cc)
except web.HTTPNotFound:
status = HTTPStatus.not_found
msg = 'not found'
except Exception as exc:
raise FetchError(cc) from exc
else:
save_flag(image, cc.lower() + '.gif')
status = HTTPStatus.ok
msg = 'OK'
if verbose and msg:
print(cc, msg)
return Result(status, cc)
@asyncio.coroutine
def downloader_coro(cc_list, base_url, verbose, concur_req):
counter = collections.Counter()
semaphore = asyncio.Semaphore(concur_req)
to_do = [download_one(cc, base_url, semaphore, verbose)
for cc in sorted(cc_list)]
to_do_iter = asyncio.as_completed(to_do)
if not verbose:
to_do_iter = tqdm.tqdm(to_do_iter, total=len(cc_list))
for future in to_do_iter:
try:
res = yield from future
except FetchError as exc:
country_code = exc.country_code
try:
error_msg = exc.__cause__.args[0]
except IndexError:
error_msg = exc.__cause__.__class__.__name__
if verbose and error_msg:
msg = '*** Error for {}: {}'
print(msg.format(country_code, error_msg))
status = HTTPStatus.error
else:
status = res.status
counter[status] += 1
return counter
def download_many(cc_list, base_url, verbose, concur_req):
loop = asyncio.get_event_loop()
coro = download_coro(cc_list, base_url, verbose, concur_req)
counts = loop.run_until_complete(wait_coro)
loop.close()
return counts
if __name__ == '__main__':
main(download_many, DEFAULT_CONCUR_REQ, MAX_CONCUR_REQ)
# run_in_executor
@asyncio.coroutine
def download_one(cc, base_url, semaphore, verbose):
try:
with (yield from semaphore):
image = yield from get_flag(base_url, cc)
except web.HTTPNotFound:
status = HTTPStatus.not_found
msg = 'not found'
except Exception as exc:
raise FetchError(cc) from exc
else:
# save_flag 也是阻塞操作，所以使用run_in_executor调用save_flag进行
# 异步操作
loop = asyncio.get_event_loop()
loop.run_in_executor(None, save_flag, image, cc.lower() + '.gif')
status = HTTPStatus.ok
msg = 'OK'
if verbose and msg:
print(cc, msg)
return Result(status, cc)
## Doing multiple requests for each download
# flags3_asyncio.py
@asyncio.coroutine
def http_get(url):
res = yield from aiohttp.request('GET', url)
if res.status == 200:
ctype = res.headers.get('Content-type', '').lower()
if 'json' in ctype or url.endswith('json'):
data = yield from res.json()
else:
data = yield from res.read()
elif res.status == 404:
raise web.HTTPNotFound()
else:
raise aiohttp.errors.HttpProcessingError(
code=res.status, message=res.reason,
headers=res.headers)
@asyncio.coroutine
def get_country(base_url, cc):
url = '{}/{cc}/metadata.json'.format(base_url, cc=cc.lower())
metadata = yield from http_get(url)
return metadata['country']
@asyncio.coroutine
def get_flag(base_url, cc):
url = '{}/{cc}/{cc}.gif'.format(base_url, cc=cc.lower())
return (yield from http_get(url))
@asyncio.coroutine
def download_one(cc, base_url, semaphore, verbose):
try:
with (yield from semaphore):
image = yield from get_flag(base_url, cc)
with (yield from semaphore):
country = yield from get_country(base_url, cc)
except web.HTTPNotFound:
status = HTTPStatus.not_found
msg = 'not found'
except Exception as exc:
raise FetchError(cc) from exc
else:
country = country.replace(' ', '_')
filename = '{}-{}.gif'.format(country, cc)
loop = asyncio.get_event_loop()
loop.run_in_executor(None, save_flag, image, filename)
status = HTTPStatus.ok
msg = 'OK'
if verbose and msg:
print(cc, msg)
return Result(status, cc) | _____no_output_____ | Apache-2.0 | notebook/fluent_ch18.ipynb | Lin0818/py-study-notebook |
Writing asyncio servers | # tcp_charfinder.py
import sys
import asyncio
from charfinder import UnicodeNameIndex
CRLF = b'\r\n'
PROMPT = b'?>'
index = UnicodeNameIndex()
@asyncio.coroutine
def handle_queries(reader, writer):
while True:
writer.write(PROMPT)
yield from writer.drain()
data = yield from reader.readline()
try:
query = data.decode().strip()
except UnicodeDecodeError:
query = '\x00'
client = writer.get_extra_info('peername')
print('Received from {}: {!r}'.format(client, query))
if query:
if ord(query[:1]) < 32:
break
lines = list(index.find_description_strs(query))
if lines:
writer.writelines(line.encode() + CRLF for line in lines)
writer.write(index.status(query, len(lines)).encode() + CRLF)
yield from writer.drain()
print('Sent {} results'.format(len(lines)))
print('Close the client socket')
writer.close()
def main(address='127.0.0.1', port=2323):
port = int(port)
loop = asyncio.get_event_loop()
server_coro = asyncio.start_server(handle_queries, address, port, loop=loop)
server = loop.run_until_complete(server_coro)
host = server.sockets[0].getsockname()
print('Serving on {}. Hit CTRL-C to stop.'.format(host))
try:
loop.run_forever()
except KeyboardInterrupt:
pass
print('Server shutting down.')
server.close()
loop.run_until_complete(server.wait_closed())
loop.close()
if __name__ == '__main__':
main()
# http_charfinder.py
@asyncio.coroutine
def init(loop, address, port):
app = web.Application(loop=loop)
app.router.add_route('GET', '/', home)
handler = app.make_handler()
server = yield from loop.create_server(handler, address, port)
return server.sockets[0].getsockname()
def home(request):
query = request.GET.get('query', '').strip()
print('Query: {!r}'.format(query))
if query:
descriptions = list(index.find_descriptions(query))
res = '\n'.join(ROW_TPL.format(**vars(descr))
for descr in descriptions)
msg = index.status(query, len(descriptions))
else:
descriptions = []
res = ''
msg = 'Enter words describing characters.'
html = template.format(query=query, result=res, message=msg)
print('Sending {} results'.format(len(descriptions)))
return web.Response(content_type=CONTENT_TYPE, text=html)
def main(address='127.0.0.1', port=8888):
port = int(port)
loop = asyncio.get_event_loop()
host = loop.run_until_complete(init(loop, address, port))
print('Serving on {}. Hit CTRL-C to stop.'.format(host))
try:
loop.run_forever()
except KeyboardInterrupt: # CTRL+C pressed
pass
print('Server shutting down.')
loop.close()
if __name__ == '__main__':
main(*sys.argv[1:]) | _____no_output_____ | Apache-2.0 | notebook/fluent_ch18.ipynb | Lin0818/py-study-notebook |
原始数据处理程序 本程序用于将原始txt格式数据以utf-8编码写入到csv文件中, 以便后续操作请在使用前确认原始数据所在文件夹内无无关文件,并修改各分类文件夹名至1-9一个可行的对应关系如下所示:财经 1 economy房产 2 realestate健康 3 health教育 4 education军事 5 military科技 6 technology体育 7 sports娱乐 8 entertainment证券 9 stock 先导入一些库 | import os #用于文件操作
import pandas as pd #用于读写数据 | _____no_output_____ | MIT | filePreprocessing.ipynb | zinccat/WeiboTextClassification |
数据处理所用函数,读取文件夹名作为数据的类别,将数据以文本(text),类别(category)的形式输出至csv文件中传入参数: corpus_path: 原始语料库根目录 out_path: 处理后文件输出目录 | def processing(corpus_path, out_path):
if not os.path.exists(out_path): #检测输出目录是否存在,若不存在则创建目录
os.makedirs(out_path)
clist = os.listdir(corpus_path) #列出原始数据根目录下的文件夹
for classid in clist: #对每个文件夹分别处理
dict = {'text': [], 'category': []}
class_path = corpus_path+classid+"/"
filelist = os.listdir(class_path)
for fileN in filelist: #处理单个文件
file_path = class_path + fileN
with open(file_path, encoding='utf-8', errors='ignore') as f:
content = f.read()
dict['text'].append(content) #将文本内容加入字典
dict['category'].append(classid) #将类别加入字典
pf = pd.DataFrame(dict, columns=["text", "category"])
if classid == '1': #第一类数据输出时创建新文件并添加header
pf.to_csv(out_path+'dataUTF8.csv', mode='w',
header=True, encoding='utf-8', index=False)
else: #将剩余类别的数据写入到已生成的文件中
pf.to_csv(out_path+'dataUTF8.csv', mode='a',
header=False, encoding='utf-8', index=False) | _____no_output_____ | MIT | filePreprocessing.ipynb | zinccat/WeiboTextClassification |
处理文件 | processing("./data/", "./dataset/") | _____no_output_____ | MIT | filePreprocessing.ipynb | zinccat/WeiboTextClassification |
Logistic Regression Table of ContentsIn this lab, we will cover logistic regression using PyTorch. Logistic Function Build a Logistic Regression Using nn.Sequential Build Custom ModulesEstimated Time Needed: 15 min Preparation We'll need the following libraries: | # Import the libraries we need for this lab
import torch.nn as nn
import torch
import matplotlib.pyplot as plt | _____no_output_____ | MIT | IBM_AI/4_Pytorch/5.1logistic_regression_prediction_v2.ipynb | merula89/cousera_notebooks |
Set the random seed: | # Set the random seed
torch.manual_seed(2) | _____no_output_____ | MIT | IBM_AI/4_Pytorch/5.1logistic_regression_prediction_v2.ipynb | merula89/cousera_notebooks |
Logistic Function Create a tensor ranging from -100 to 100: | z = torch.arange(-100, 100, 0.1).view(-1, 1)
print("The tensor: ", z) | The tensor: tensor([[-100.0000],
[ -99.9000],
[ -99.8000],
...,
[ 99.7000],
[ 99.8000],
[ 99.9000]])
| MIT | IBM_AI/4_Pytorch/5.1logistic_regression_prediction_v2.ipynb | merula89/cousera_notebooks |
Create a sigmoid object: | # Create sigmoid object
sig = nn.Sigmoid() | _____no_output_____ | MIT | IBM_AI/4_Pytorch/5.1logistic_regression_prediction_v2.ipynb | merula89/cousera_notebooks |
Apply the element-wise function Sigmoid with the object: | # Use sigmoid object to calculate the
yhat = sig(z) | _____no_output_____ | MIT | IBM_AI/4_Pytorch/5.1logistic_regression_prediction_v2.ipynb | merula89/cousera_notebooks |
Plot the results: | plt.plot(z.numpy(), yhat.numpy())
plt.xlabel('z')
plt.ylabel('yhat') | _____no_output_____ | MIT | IBM_AI/4_Pytorch/5.1logistic_regression_prediction_v2.ipynb | merula89/cousera_notebooks |
Apply the element-wise Sigmoid from the function module and plot the results: | yhat = torch.sigmoid(z)
plt.plot(z.numpy(), yhat.numpy()) | _____no_output_____ | MIT | IBM_AI/4_Pytorch/5.1logistic_regression_prediction_v2.ipynb | merula89/cousera_notebooks |
Build a Logistic Regression with nn.Sequential Create a 1x1 tensor where x represents one data sample with one dimension, and 2x1 tensor X represents two data samples of one dimension: | # Create x and X tensor
x = torch.tensor([[1.0]])
X = torch.tensor([[1.0], [100]])
print('x = ', x)
print('X = ', X) | x = tensor([[1.]])
X = tensor([[ 1.],
[100.]])
| MIT | IBM_AI/4_Pytorch/5.1logistic_regression_prediction_v2.ipynb | merula89/cousera_notebooks |
Create a logistic regression object with the nn.Sequential model with a one-dimensional input: | # Use sequential function to create model
model = nn.Sequential(nn.Linear(1, 1), nn.Sigmoid()) | _____no_output_____ | MIT | IBM_AI/4_Pytorch/5.1logistic_regression_prediction_v2.ipynb | merula89/cousera_notebooks |
The object is represented in the following diagram: In this case, the parameters are randomly initialized. You can view them the following ways: | # Print the parameters
print("list(model.parameters()):\n ", list(model.parameters()))
print("\nmodel.state_dict():\n ", model.state_dict()) | list(model.parameters()):
[Parameter containing:
tensor([[0.2294]], requires_grad=True), Parameter containing:
tensor([-0.2380], requires_grad=True)]
model.state_dict():
OrderedDict([('0.weight', tensor([[0.2294]])), ('0.bias', tensor([-0.2380]))])
| MIT | IBM_AI/4_Pytorch/5.1logistic_regression_prediction_v2.ipynb | merula89/cousera_notebooks |
Make a prediction with one sample: | # The prediction for x
yhat = model(x)
print("The prediction: ", yhat) | The prediction: tensor([[0.4979]], grad_fn=<SigmoidBackward>)
| MIT | IBM_AI/4_Pytorch/5.1logistic_regression_prediction_v2.ipynb | merula89/cousera_notebooks |
Calling the object with tensor X performed the following operation (code values may not be the same as the diagrams value depending on the version of PyTorch) : Make a prediction with multiple samples: | # The prediction for X
yhat = model(X)
yhat | _____no_output_____ | MIT | IBM_AI/4_Pytorch/5.1logistic_regression_prediction_v2.ipynb | merula89/cousera_notebooks |
Calling the object performed the following operation: Create a 1x2 tensor where x represents one data sample with one dimension, and 2x3 tensor X represents one data sample of two dimensions: | # Create and print samples
x = torch.tensor([[1.0, 1.0]])
X = torch.tensor([[1.0, 1.0], [1.0, 2.0], [1.0, 3.0]])
print('x = ', x)
print('X = ', X) | x = tensor([[1., 1.]])
X = tensor([[1., 1.],
[1., 2.],
[1., 3.]])
| MIT | IBM_AI/4_Pytorch/5.1logistic_regression_prediction_v2.ipynb | merula89/cousera_notebooks |
Create a logistic regression object with the nn.Sequential model with a two-dimensional input: | # Create new model using nn.sequential()
model = nn.Sequential(nn.Linear(2, 1), nn.Sigmoid()) | _____no_output_____ | MIT | IBM_AI/4_Pytorch/5.1logistic_regression_prediction_v2.ipynb | merula89/cousera_notebooks |
The object will apply the Sigmoid function to the output of the linear function as shown in the following diagram: In this case, the parameters are randomly initialized. You can view them the following ways: | # Print the parameters
print("list(model.parameters()):\n ", list(model.parameters()))
print("\nmodel.state_dict():\n ", model.state_dict()) | list(model.parameters()):
[Parameter containing:
tensor([[ 0.1939, -0.0361]], requires_grad=True), Parameter containing:
tensor([0.3021], requires_grad=True)]
model.state_dict():
OrderedDict([('0.weight', tensor([[ 0.1939, -0.0361]])), ('0.bias', tensor([0.3021]))])
| MIT | IBM_AI/4_Pytorch/5.1logistic_regression_prediction_v2.ipynb | merula89/cousera_notebooks |
Make a prediction with one sample: | # Make the prediction of x
yhat = model(x)
print("The prediction: ", yhat) | The prediction: tensor([[0.6130]], grad_fn=<SigmoidBackward>)
| MIT | IBM_AI/4_Pytorch/5.1logistic_regression_prediction_v2.ipynb | merula89/cousera_notebooks |
The operation is represented in the following diagram: Make a prediction with multiple samples: | # The prediction of X
yhat = model(X)
print("The prediction: ", yhat) | _____no_output_____ | MIT | IBM_AI/4_Pytorch/5.1logistic_regression_prediction_v2.ipynb | merula89/cousera_notebooks |
The operation is represented in the following diagram: Build Custom Modules In this section, you will build a custom Module or class. The model or object function is identical to using nn.Sequential. Create a logistic regression custom module: | # Create logistic_regression custom class
class logistic_regression(nn.Module):
# Constructor
def __init__(self, n_inputs):
super(logistic_regression, self).__init__()
self.linear = nn.Linear(n_inputs, 1)
# Prediction
def forward(self, x):
yhat = torch.sigmoid(self.linear(x))
return yhat | _____no_output_____ | MIT | IBM_AI/4_Pytorch/5.1logistic_regression_prediction_v2.ipynb | merula89/cousera_notebooks |
Create a 1x1 tensor where x represents one data sample with one dimension, and 3x1 tensor where $X$ represents one data sample of one dimension: | # Create x and X tensor
x = torch.tensor([[1.0]])
X = torch.tensor([[-100], [0], [100.0]])
print('x = ', x)
print('X = ', X) | _____no_output_____ | MIT | IBM_AI/4_Pytorch/5.1logistic_regression_prediction_v2.ipynb | merula89/cousera_notebooks |
Create a model to predict one dimension: | # Create logistic regression model
model = logistic_regression(1) | _____no_output_____ | MIT | IBM_AI/4_Pytorch/5.1logistic_regression_prediction_v2.ipynb | merula89/cousera_notebooks |
In this case, the parameters are randomly initialized. You can view them the following ways: | # Print parameters
print("list(model.parameters()):\n ", list(model.parameters()))
print("\nmodel.state_dict():\n ", model.state_dict()) | _____no_output_____ | MIT | IBM_AI/4_Pytorch/5.1logistic_regression_prediction_v2.ipynb | merula89/cousera_notebooks |
Make a prediction with one sample: | # Make the prediction of x
yhat = model(x)
print("The prediction result: \n", yhat) | _____no_output_____ | MIT | IBM_AI/4_Pytorch/5.1logistic_regression_prediction_v2.ipynb | merula89/cousera_notebooks |
Make a prediction with multiple samples: | # Make the prediction of X
yhat = model(X)
print("The prediction result: \n", yhat) | _____no_output_____ | MIT | IBM_AI/4_Pytorch/5.1logistic_regression_prediction_v2.ipynb | merula89/cousera_notebooks |
Create a logistic regression object with a function with two inputs: | # Create logistic regression model
model = logistic_regression(2) | _____no_output_____ | MIT | IBM_AI/4_Pytorch/5.1logistic_regression_prediction_v2.ipynb | merula89/cousera_notebooks |
Create a 1x2 tensor where x represents one data sample with one dimension, and 3x2 tensor X represents one data sample of one dimension: | # Create x and X tensor
x = torch.tensor([[1.0, 2.0]])
X = torch.tensor([[100, -100], [0.0, 0.0], [-100, 100]])
print('x = ', x)
print('X = ', X) | _____no_output_____ | MIT | IBM_AI/4_Pytorch/5.1logistic_regression_prediction_v2.ipynb | merula89/cousera_notebooks |
Make a prediction with one sample: | # Make the prediction of x
yhat = model(x)
print("The prediction result: \n", yhat) | _____no_output_____ | MIT | IBM_AI/4_Pytorch/5.1logistic_regression_prediction_v2.ipynb | merula89/cousera_notebooks |
Make a prediction with multiple samples: | # Make the prediction of X
yhat = model(X)
print("The prediction result: \n", yhat) | _____no_output_____ | MIT | IBM_AI/4_Pytorch/5.1logistic_regression_prediction_v2.ipynb | merula89/cousera_notebooks |
Practice Make your own model my_model as applying linear regression first and then logistic regression using nn.Sequential(). Print out your prediction. | # Practice: Make your model and make the prediction
X = torch.tensor([-10.0]) | _____no_output_____ | MIT | IBM_AI/4_Pytorch/5.1logistic_regression_prediction_v2.ipynb | merula89/cousera_notebooks |
Classification on Iris dataset with sklearn and DJLIn this notebook, you will try to use a pre-trained sklearn model to run on DJL for a general classification task. The model was trained with [Iris flower dataset](https://en.wikipedia.org/wiki/Iris_flower_data_set). Background Iris DatasetThe dataset contains a set of 150 records under five attributes - sepal length, sepal width, petal length, petal width and species.Iris setosa | Iris versicolor | Iris virginica:-------------------------:|:-------------------------:|:-------------------------: |  |  The chart above shows three different kinds of the Iris flowers. We will use sepal length, sepal width, petal length, petal width as the feature and species as the label to train the model. Sklearn ModelYou can find more information [here](http://onnx.ai/sklearn-onnx/). You can use the sklearn built-in iris dataset to load the data. Then we defined a [RandomForestClassifer](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html) to train the model. After that, we convert the model to onnx format for DJL to run inference. The following code is a sample classification setup using sklearn:```python Train a model.from sklearn.datasets import load_irisfrom sklearn.model_selection import train_test_splitfrom sklearn.ensemble import RandomForestClassifieriris = load_iris()X, y = iris.data, iris.targetX_train, X_test, y_train, y_test = train_test_split(X, y)clr = RandomForestClassifier()clr.fit(X_train, y_train)``` PreparationThis tutorial requires the installation of Java Kernel. To install the Java Kernel, see the [README](https://github.com/awslabs/djl/blob/master/jupyter/README.md).These are dependencies we will use. To enhance the NDArray operation capability, we are importing ONNX Runtime and PyTorch Engine at the same time. Please find more information [here](https://github.com/awslabs/djl/blob/master/docs/onnxruntime/hybrid_engine.mdhybrid-engine-for-onnx-runtime). | // %mavenRepo snapshots https://oss.sonatype.org/content/repositories/snapshots/
%maven ai.djl:api:0.8.0
%maven ai.djl.onnxruntime:onnxruntime-engine:0.8.0
%maven ai.djl.pytorch:pytorch-engine:0.8.0
%maven org.slf4j:slf4j-api:1.7.26
%maven org.slf4j:slf4j-simple:1.7.26
%maven com.microsoft.onnxruntime:onnxruntime:1.4.0
%maven ai.djl.pytorch:pytorch-native-auto:1.6.0
import ai.djl.inference.*;
import ai.djl.modality.*;
import ai.djl.ndarray.*;
import ai.djl.ndarray.types.*;
import ai.djl.repository.zoo.*;
import ai.djl.translate.*;
import java.util.*; | _____no_output_____ | Apache-2.0 | jupyter/onnxruntime/machine_learning_with_ONNXRuntime.ipynb | raghav-deepsource/djl |
Step 1 create a TranslatorInference in machine learning is the process of predicting the output for a given input based on a pre-defined model.DJL abstracts away the whole process for ease of use. It can load the model, perform inference on the input, and provideoutput. DJL also allows you to provide user-defined inputs. The workflow looks like the following:The `Translator` interface encompasses the two white blocks: Pre-processing and Post-processing. The pre-processingcomponent converts the user-defined input objects into an NDList, so that the `Predictor` in DJL can understand theinput and make its prediction. Similarly, the post-processing block receives an NDList as the output from the`Predictor`. The post-processing block allows you to convert the output from the `Predictor` to the desired outputformat.In our use case, we use a class namely `IrisFlower` as our input class type. We will use [`Classifications`](https://javadoc.io/doc/ai.djl/api/latest/ai/djl/modality/Classifications.html) as our output class type. | public static class IrisFlower {
public float sepalLength;
public float sepalWidth;
public float petalLength;
public float petalWidth;
public IrisFlower(float sepalLength, float sepalWidth, float petalLength, float petalWidth) {
this.sepalLength = sepalLength;
this.sepalWidth = sepalWidth;
this.petalLength = petalLength;
this.petalWidth = petalWidth;
}
} | _____no_output_____ | Apache-2.0 | jupyter/onnxruntime/machine_learning_with_ONNXRuntime.ipynb | raghav-deepsource/djl |
Let's create a translator | public static class MyTranslator implements Translator<IrisFlower, Classifications> {
private final List<String> synset;
public MyTranslator() {
// species name
synset = Arrays.asList("setosa", "versicolor", "virginica");
}
@Override
public NDList processInput(TranslatorContext ctx, IrisFlower input) {
float[] data = {input.sepalLength, input.sepalWidth, input.petalLength, input.petalWidth};
NDArray array = ctx.getNDManager().create(data, new Shape(1, 4));
return new NDList(array);
}
@Override
public Classifications processOutput(TranslatorContext ctx, NDList list) {
return new Classifications(synset, list.get(1));
}
@Override
public Batchifier getBatchifier() {
return null;
}
} | _____no_output_____ | Apache-2.0 | jupyter/onnxruntime/machine_learning_with_ONNXRuntime.ipynb | raghav-deepsource/djl |
Step 2 Prepare your modelWe will load a pretrained sklearn model into DJL. We defined a [`ModelZoo`](https://javadoc.io/doc/ai.djl/api/latest/ai/djl/repository/zoo/ModelZoo.html) concept to allow user load model from varity of locations, such as remote URL, local files or DJL pretrained model zoo. We need to define `Criteria` class to help the modelzoo locate the model and attach translator. In this example, we download a compressed ONNX model from S3. | String modelUrl = "https://mlrepo.djl.ai/model/tabular/random_forest/ai/djl/onnxruntime/iris_flowers/0.0.1/iris_flowers.zip";
Criteria<IrisFlower, Classifications> criteria = Criteria.builder()
.setTypes(IrisFlower.class, Classifications.class)
.optModelUrls(modelUrl)
.optTranslator(new MyTranslator())
.optEngine("OnnxRuntime") // use OnnxRuntime engine by default
.build();
ZooModel<IrisFlower, Classifications> model = ModelZoo.loadModel(criteria); | _____no_output_____ | Apache-2.0 | jupyter/onnxruntime/machine_learning_with_ONNXRuntime.ipynb | raghav-deepsource/djl |
Step 3 Run inferenceUser will just need to create a `Predictor` from model to run the inference. | Predictor<IrisFlower, Classifications> predictor = model.newPredictor();
IrisFlower info = new IrisFlower(1.0f, 2.0f, 3.0f, 4.0f);
predictor.predict(info); | _____no_output_____ | Apache-2.0 | jupyter/onnxruntime/machine_learning_with_ONNXRuntime.ipynb | raghav-deepsource/djl |
View source on GitHub Notebook Viewer Run in Google Colab Install Earth Engine API and geemapInstall the [Earth Engine Python API](https://developers.google.com/earth-engine/python_install) and [geemap](https://geemap.org). The **geemap** Python package is built upon the [ipyleaflet](https://github.com/jupyter-widgets/ipyleaflet) and [folium](https://github.com/python-visualization/folium) packages and implements several methods for interacting with Earth Engine data layers, such as `Map.addLayer()`, `Map.setCenter()`, and `Map.centerObject()`.The following script checks if the geemap package has been installed. If not, it will install geemap, which automatically installs its [dependencies](https://github.com/giswqs/geemapdependencies), including earthengine-api, folium, and ipyleaflet. | # Installs geemap package
import subprocess
try:
import geemap
except ImportError:
print('Installing geemap ...')
subprocess.check_call(["python", '-m', 'pip', 'install', 'geemap'])
import ee
import geemap | _____no_output_____ | MIT | Algorithms/landsat_radiance.ipynb | OIEIEIO/earthengine-py-notebooks |
Create an interactive map The default basemap is `Google Maps`. [Additional basemaps](https://github.com/giswqs/geemap/blob/master/geemap/basemaps.py) can be added using the `Map.add_basemap()` function. | Map = geemap.Map(center=[40,-100], zoom=4)
Map | _____no_output_____ | MIT | Algorithms/landsat_radiance.ipynb | OIEIEIO/earthengine-py-notebooks |
Add Earth Engine Python script | # Add Earth Engine dataset
# Load a raw Landsat scene and display it.
raw = ee.Image('LANDSAT/LC08/C01/T1/LC08_044034_20140318')
Map.centerObject(raw, 10)
Map.addLayer(raw, {'bands': ['B4', 'B3', 'B2'], 'min': 6000, 'max': 12000}, 'raw')
# Convert the raw data to radiance.
radiance = ee.Algorithms.Landsat.calibratedRadiance(raw)
Map.addLayer(radiance, {'bands': ['B4', 'B3', 'B2'], 'max': 90}, 'radiance')
# Convert the raw data to top-of-atmosphere reflectance.
toa = ee.Algorithms.Landsat.TOA(raw)
Map.addLayer(toa, {'bands': ['B4', 'B3', 'B2'], 'max': 0.2}, 'toa reflectance')
| _____no_output_____ | MIT | Algorithms/landsat_radiance.ipynb | OIEIEIO/earthengine-py-notebooks |
Display Earth Engine data layers | Map.addLayerControl() # This line is not needed for ipyleaflet-based Map.
Map | _____no_output_____ | MIT | Algorithms/landsat_radiance.ipynb | OIEIEIO/earthengine-py-notebooks |
Import Libraries | from __future__ import print_function
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import torchvision
from torchvision import datasets, transforms
%matplotlib inline
import matplotlib.pyplot as plt | _____no_output_____ | MIT | MNIST/Session2/3_Global_Average_Pooling.ipynb | gmshashank/pytorch_vision |
Data TransformationsWe first start with defining our data transformations. We need to think what our data is and how can we augment it to correct represent images which it might not see otherwise. | # Train Phase transformations
train_transforms = transforms.Compose([
# transforms.Resize((28, 28)),
# transforms.ColorJitter(brightness=0.10, contrast=0.1, saturation=0.10, hue=0.1),
transforms.ToTensor(),
transforms.Normalize((0.1307,), (0.3081,)) # The mean and std have to be sequences (e.g., tuples), therefore you should add a comma after the values.
# Note the difference between (0.1307) and (0.1307,)
])
# Test Phase transformations
test_transforms = transforms.Compose([
# transforms.Resize((28, 28)),
# transforms.ColorJitter(brightness=0.10, contrast=0.1, saturation=0.10, hue=0.1),
transforms.ToTensor(),
transforms.Normalize((0.1307,), (0.3081,))
])
| _____no_output_____ | MIT | MNIST/Session2/3_Global_Average_Pooling.ipynb | gmshashank/pytorch_vision |
Dataset and Creating Train/Test Split | train = datasets.MNIST('./data', train=True, download=True, transform=train_transforms)
test = datasets.MNIST('./data', train=False, download=True, transform=test_transforms) | Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz to ./data/MNIST/raw/train-images-idx3-ubyte.gz
| MIT | MNIST/Session2/3_Global_Average_Pooling.ipynb | gmshashank/pytorch_vision |
Dataloader Arguments & Test/Train Dataloaders | SEED = 1
# CUDA?
cuda = torch.cuda.is_available()
print("CUDA Available?", cuda)
# For reproducibility
torch.manual_seed(SEED)
if cuda:
torch.cuda.manual_seed(SEED)
# dataloader arguments - something you'll fetch these from cmdprmt
dataloader_args = dict(shuffle=True, batch_size=128, num_workers=4, pin_memory=True) if cuda else dict(shuffle=True, batch_size=64)
# train dataloader
train_loader = torch.utils.data.DataLoader(train, **dataloader_args)
# test dataloader
test_loader = torch.utils.data.DataLoader(test, **dataloader_args) | CUDA Available? True
| MIT | MNIST/Session2/3_Global_Average_Pooling.ipynb | gmshashank/pytorch_vision |
Data StatisticsIt is important to know your data very well. Let's check some of the statistics around our data and how it actually looks like | # We'd need to convert it into Numpy! Remember above we have converted it into tensors already
train_data = train.train_data
train_data = train.transform(train_data.numpy())
print('[Train]')
print(' - Numpy Shape:', train.train_data.cpu().numpy().shape)
print(' - Tensor Shape:', train.train_data.size())
print(' - min:', torch.min(train_data))
print(' - max:', torch.max(train_data))
print(' - mean:', torch.mean(train_data))
print(' - std:', torch.std(train_data))
print(' - var:', torch.var(train_data))
dataiter = iter(train_loader)
images, labels = dataiter.next()
print(images.shape)
print(labels.shape)
# Let's visualize some of the images
plt.imshow(images[0].numpy().squeeze(), cmap='gray_r') | MIT | MNIST/Session2/3_Global_Average_Pooling.ipynb | gmshashank/pytorch_vision |
|
MOREIt is important that we view as many images as possible. This is required to get some idea on image augmentation later on | figure = plt.figure()
num_of_images = 60
for index in range(1, num_of_images + 1):
plt.subplot(6, 10, index)
plt.axis('off')
plt.imshow(images[index].numpy().squeeze(), cmap='gray_r') | _____no_output_____ | MIT | MNIST/Session2/3_Global_Average_Pooling.ipynb | gmshashank/pytorch_vision |
The modelLet's start with the model we first saw | class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
# Input Block
self.convblock1 = nn.Sequential(
nn.Conv2d(in_channels=1, out_channels=16, kernel_size=(3, 3), padding=0, bias=False),
nn.ReLU(),
) # output_size = 26
# CONVOLUTION BLOCK 1
self.convblock2 = nn.Sequential(
nn.Conv2d(in_channels=16, out_channels=16, kernel_size=(3, 3), padding=0, bias=False),
nn.ReLU(),
) # output_size = 24
# TRANSITION BLOCK 1
self.convblock3 = nn.Sequential(
nn.Conv2d(in_channels=16, out_channels=16, kernel_size=(1, 1), padding=0, bias=False),
nn.ReLU(),
) # output_size = 24
self.pool1 = nn.MaxPool2d(2, 2) # output_size = 12
# CONVOLUTION BLOCK 2
self.convblock4 = nn.Sequential(
nn.Conv2d(in_channels=16, out_channels=16, kernel_size=(3, 3), padding=0, bias=False),
nn.ReLU(),
) # output_size = 10
self.convblock5 = nn.Sequential(
nn.Conv2d(in_channels=16, out_channels=16, kernel_size=(3, 3), padding=0, bias=False),
nn.ReLU(),
) # output_size = 8
self.convblock6 = nn.Sequential(
nn.Conv2d(in_channels=16, out_channels=10, kernel_size=(3, 3), padding=0, bias=False),
nn.ReLU(),
) # output_size = 6
# OUTPUT BLOCK
self.convblock7 = nn.Sequential(
nn.Conv2d(in_channels=10, out_channels=10, kernel_size=(3, 3), padding=1, bias=False),
nn.ReLU(),
) # output_size = 6
self.gap = nn.Sequential(
nn.AvgPool2d(kernel_size=6)
)
self.convblock8 = nn.Sequential(
nn.Conv2d(in_channels=10, out_channels=10, kernel_size=(1, 1), padding=0, bias=False),
# nn.BatchNorm2d(10), NEVER
# nn.ReLU() NEVER!
) # output_size = 1
def forward(self, x):
x = self.convblock1(x)
x = self.convblock2(x)
x = self.convblock3(x)
x = self.pool1(x)
x = self.convblock4(x)
x = self.convblock5(x)
x = self.convblock6(x)
x = self.convblock7(x)
x = self.gap(x)
x = self.convblock8(x)
x = x.view(-1, 10)
return F.log_softmax(x, dim=-1) | _____no_output_____ | MIT | MNIST/Session2/3_Global_Average_Pooling.ipynb | gmshashank/pytorch_vision |
Model ParamsCan't emphasize on how important viewing Model Summary is. Unfortunately, there is no in-built model visualizer, so we have to take external help | !pip install torchsummary
from torchsummary import summary
use_cuda = torch.cuda.is_available()
device = torch.device("cuda" if use_cuda else "cpu")
print(device)
model = Net().to(device)
summary(model, input_size=(1, 28, 28))
| Requirement already satisfied: torchsummary in /usr/local/lib/python3.6/dist-packages (1.5.1)
cuda
----------------------------------------------------------------
Layer (type) Output Shape Param #
================================================================
Conv2d-1 [-1, 16, 26, 26] 144
ReLU-2 [-1, 16, 26, 26] 0
Conv2d-3 [-1, 16, 24, 24] 2,304
ReLU-4 [-1, 16, 24, 24] 0
Conv2d-5 [-1, 16, 24, 24] 256
ReLU-6 [-1, 16, 24, 24] 0
MaxPool2d-7 [-1, 16, 12, 12] 0
Conv2d-8 [-1, 16, 10, 10] 2,304
ReLU-9 [-1, 16, 10, 10] 0
Conv2d-10 [-1, 16, 8, 8] 2,304
ReLU-11 [-1, 16, 8, 8] 0
Conv2d-12 [-1, 10, 6, 6] 1,440
ReLU-13 [-1, 10, 6, 6] 0
Conv2d-14 [-1, 10, 6, 6] 900
ReLU-15 [-1, 10, 6, 6] 0
AvgPool2d-16 [-1, 10, 1, 1] 0
Conv2d-17 [-1, 10, 1, 1] 100
================================================================
Total params: 9,752
Trainable params: 9,752
Non-trainable params: 0
----------------------------------------------------------------
Input size (MB): 0.00
Forward/backward pass size (MB): 0.52
Params size (MB): 0.04
Estimated Total Size (MB): 0.56
----------------------------------------------------------------
| MIT | MNIST/Session2/3_Global_Average_Pooling.ipynb | gmshashank/pytorch_vision |
Training and TestingLooking at logs can be boring, so we'll introduce **tqdm** progressbar to get cooler logs. Let's write train and test functions | from tqdm import tqdm
train_losses = []
test_losses = []
train_acc = []
test_acc = []
def train(model, device, train_loader, optimizer, epoch):
global train_max
model.train()
pbar = tqdm(train_loader)
correct = 0
processed = 0
for batch_idx, (data, target) in enumerate(pbar):
# get samples
data, target = data.to(device), target.to(device)
# Init
optimizer.zero_grad()
# In PyTorch, we need to set the gradients to zero before starting to do backpropragation because PyTorch accumulates the gradients on subsequent backward passes.
# Because of this, when you start your training loop, ideally you should zero out the gradients so that you do the parameter update correctly.
# Predict
y_pred = model(data)
# Calculate loss
loss = F.nll_loss(y_pred, target)
train_losses.append(loss)
# Backpropagation
loss.backward()
optimizer.step()
# Update pbar-tqdm
pred = y_pred.argmax(dim=1, keepdim=True) # get the index of the max log-probability
correct += pred.eq(target.view_as(pred)).sum().item()
processed += len(data)
pbar.set_description(desc= f'Loss={loss.item()} Batch_id={batch_idx} Accuracy={100*correct/processed:0.2f}')
train_acc.append(100*correct/processed)
if (train_max < 100*correct/processed):
train_max = 100*correct/processed
def test(model, device, test_loader):
global test_max
model.eval()
test_loss = 0
correct = 0
with torch.no_grad():
for data, target in test_loader:
data, target = data.to(device), target.to(device)
output = model(data)
test_loss += F.nll_loss(output, target, reduction='sum').item() # sum up batch loss
pred = output.argmax(dim=1, keepdim=True) # get the index of the max log-probability
correct += pred.eq(target.view_as(pred)).sum().item()
test_loss /= len(test_loader.dataset)
test_losses.append(test_loss)
print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.2f}%)\n'.format(
test_loss, correct, len(test_loader.dataset),
100. * correct / len(test_loader.dataset)))
if (test_max < 100. * correct / len(test_loader.dataset)):
test_max = 100. * correct / len(test_loader.dataset)
test_acc.append(100. * correct / len(test_loader.dataset))
| _____no_output_____ | MIT | MNIST/Session2/3_Global_Average_Pooling.ipynb | gmshashank/pytorch_vision |
Let's Train and test our model | model = Net().to(device)
optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.9)
EPOCHS = 15
train_max=0
test_max=0
for epoch in range(EPOCHS):
print("EPOCH:", epoch)
train(model, device, train_loader, optimizer, epoch)
test(model, device, test_loader)
print(f"\nMaximum training accuracy: {train_max}\n")
print(f"\nMaximum test accuracy: {test_max}\n")
fig, axs = plt.subplots(2,2,figsize=(15,10))
axs[0, 0].plot(train_losses)
axs[0, 0].set_title("Training Loss")
axs[1, 0].plot(train_acc)
axs[1, 0].set_title("Training Accuracy")
axs[0, 1].plot(test_losses)
axs[0, 1].set_title("Test Loss")
axs[1, 1].plot(test_acc)
axs[1, 1].set_title("Test Accuracy")
fig, ((axs1, axs2), (axs3, axs4)) = plt.subplots(2,2,figsize=(15,10))
# Train plot
axs1.plot(train_losses)
axs1.set_title("Training Loss")
axs3.plot(train_acc)
axs3.set_title("Training Accuracy")
# axs1.set_xlim([0, 5])
axs1.set_ylim([0, 5])
axs3.set_ylim([0, 100])
# Test plot
axs2.plot(test_losses)
axs2.set_title("Test Loss")
axs4.plot(test_acc)
axs4.set_title("Test Accuracy")
axs2.set_ylim([0, 5])
axs4.set_ylim([0, 100])
| _____no_output_____ | MIT | MNIST/Session2/3_Global_Average_Pooling.ipynb | gmshashank/pytorch_vision |
basic operation on image | import cv2
import numpy as np
impath = r"D:/Study/example_ml/computer_vision_example/cv_exercise/opencv-master/samples/data/messi5.jpg"
img = cv2.imread(impath)
print(img.shape)
print(img.size)
print(img.dtype)
b,g,r = cv2.split(img)
img = cv2.merge((b,g,r))
cv2.imshow("image",img)
cv2.waitKey(0)
cv2.destroyAllWindows() | _____no_output_____ | Apache-2.0 | exercise_2.ipynb | deepak223098/Computer_Vision_Example |
copy and paste | import cv2
import numpy as np
impath = r"D:/Study/example_ml/computer_vision_example/cv_exercise/opencv-master/samples/data/messi5.jpg"
img = cv2.imread(impath)
'''b,g,r = cv2.split(img)
img = cv2.merge((b,g,r))'''
ball = img[280:340,330:390]
img[273:333,100:160] = ball
cv2.imshow("image",img)
cv2.waitKey(0)
cv2.destroyAllWindows() | _____no_output_____ | Apache-2.0 | exercise_2.ipynb | deepak223098/Computer_Vision_Example |
merge two imge | import cv2
import numpy as np
impath = r"D:/Study/example_ml/computer_vision_example/cv_exercise/opencv-master/samples/data/messi5.jpg"
impath1 = r"D:/Study/example_ml/computer_vision_example/cv_exercise/opencv-master/samples/data/opencv-logo.png"
img = cv2.imread(impath)
img1 = cv2.imread(impath1)
img = cv2.resize(img, (512,512))
img1 = cv2.resize(img1, (512,512))
#new_img = cv2.add(img,img1)
new_img = cv2.addWeighted(img,0.1,img1,0.8,1)
cv2.imshow("new_image",new_img)
cv2.waitKey(0)
cv2.destroyAllWindows() | _____no_output_____ | Apache-2.0 | exercise_2.ipynb | deepak223098/Computer_Vision_Example |
bitwise operation | import cv2
import numpy as np
img1 = np.zeros([250,500,3],np.uint8)
img1 = cv2.rectangle(img1,(200,0),(300,100),(255,255,255),-1)
img2 = np.full((250, 500, 3), 255, dtype=np.uint8)
img2 = cv2.rectangle(img2, (0, 0), (250, 250), (0, 0, 0), -1)
#bit_and = cv2.bitwise_and(img2,img1)
#bit_or = cv2.bitwise_or(img2,img1)
#bit_xor = cv2.bitwise_xor(img2,img1)
bit_not = cv2.bitwise_not(img2)
#cv2.imshow("bit_and",bit_and)
#cv2.imshow("bit_or",bit_or)
#cv2.imshow("bit_xor",bit_xor)
cv2.imshow("bit_not",bit_not)
cv2.imshow("img1",img1)
cv2.imshow("img2",img2)
cv2.waitKey(0)
cv2.destroyAllWindows() | _____no_output_____ | Apache-2.0 | exercise_2.ipynb | deepak223098/Computer_Vision_Example |
simple thresholding THRESH_BINARY | import cv2
import numpy as np
img = cv2.imread('gradient.jpg',0)
_,th1 = cv2.threshold(img,127,255,cv2.THRESH_BINARY) #check every pixel with 127
cv2.imshow("img",img)
cv2.imshow("th1",th1)
cv2.waitKey(0)
cv2.destroyAllWindows() | _____no_output_____ | Apache-2.0 | exercise_2.ipynb | deepak223098/Computer_Vision_Example |
THRESH_BINARY_INV | import cv2
import numpy as np
img = cv2.imread('gradient.jpg',0)
_,th1 = cv2.threshold(img,127,255,cv2.THRESH_BINARY)
_,th2 = cv2.threshold(img,127,255,cv2.THRESH_BINARY_INV) #check every pixel with 127
cv2.imshow("img",img)
cv2.imshow("th1",th1)
cv2.imshow("th2",th2)
cv2.waitKey(0)
cv2.destroyAllWindows() | _____no_output_____ | Apache-2.0 | exercise_2.ipynb | deepak223098/Computer_Vision_Example |
THRESH_TRUNC | import cv2
import numpy as np
img = cv2.imread('gradient.jpg',0)
_,th1 = cv2.threshold(img,127,255,cv2.THRESH_BINARY)
_,th2 = cv2.threshold(img,255,255,cv2.THRESH_TRUNC) #check every pixel with 127
cv2.imshow("img",img)
cv2.imshow("th1",th1)
cv2.imshow("th2",th2)
cv2.waitKey(0)
cv2.destroyAllWindows() | _____no_output_____ | Apache-2.0 | exercise_2.ipynb | deepak223098/Computer_Vision_Example |
THRESH_TOZERO | import cv2
import numpy as np
img = cv2.imread('gradient.jpg',0)
_,th1 = cv2.threshold(img,127,255,cv2.THRESH_BINARY)
_,th2 = cv2.threshold(img,127,255,cv2.THRESH_TOZERO) #check every pixel with 127
_,th3 = cv2.threshold(img,127,255,cv2.THRESH_TOZERO_INV) #check every pixel with 127
cv2.imshow("img",img)
cv2.imshow("th1",th1)
cv2.imshow("th2",th2)
cv2.imshow("th3",th3)
cv2.waitKey(0)
cv2.destroyAllWindows() | _____no_output_____ | Apache-2.0 | exercise_2.ipynb | deepak223098/Computer_Vision_Example |
Adaptive Thresholding it will calculate the threshold for smaller region of iamge .so we get different thresholding value for different region of same image | import cv2
import numpy as np
img = cv2.imread('sudoku1.jpg')
img = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
_,th1 = cv2.threshold(img,127,255,cv2.THRESH_BINARY)
th2 = cv2.adaptiveThreshold(img,255,cv2.ADAPTIVE_THRESH_MEAN_C,
cv2.THRESH_BINARY,11,2)
th3 = cv2.adaptiveThreshold(img,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C,
cv2.THRESH_BINARY,11,2)
cv2.imshow("img",img)
cv2.imshow("THRESH_BINARY",th1)
cv2.imshow("ADAPTIVE_THRESH_MEAN_C",th2)
cv2.imshow("ADAPTIVE_THRESH_GAUSSIAN_C",th3)
cv2.waitKey(0)
cv2.destroyAllWindows() | _____no_output_____ | Apache-2.0 | exercise_2.ipynb | deepak223098/Computer_Vision_Example |
Morphological Transformations Morphological Transformations are some simple operation based on the image shape. Morphological Transformations are normally performed on binary images. A kernal tells you how to change the value of any given pixel by combining it with different amounts of the neighbouring pixels. | import cv2
%matplotlib notebook
%matplotlib inline
from matplotlib import pyplot as plt
img = cv2.imread("hsv_ball.jpg",cv2.IMREAD_GRAYSCALE)
_,mask = cv2.threshold(img, 220,255,cv2.THRESH_BINARY_INV)
titles = ['images',"mask"]
images = [img,mask]
for i in range(2):
plt.subplot(1,2,i+1)
plt.imshow(images[i],"gray")
plt.title(titles[i])
plt.show() | _____no_output_____ | Apache-2.0 | exercise_2.ipynb | deepak223098/Computer_Vision_Example |
Morphological Transformations using erosion | import cv2
import numpy as np
%matplotlib notebook
%matplotlib inline
from matplotlib import pyplot as plt
img = cv2.imread("hsv_ball.jpg",cv2.IMREAD_GRAYSCALE)
_,mask = cv2.threshold(img, 220,255,cv2.THRESH_BINARY_INV)
kernal = np.ones((2,2),np.uint8)
dilation = cv2.dilate(mask,kernal,iterations = 3)
erosion = cv2.erode(mask,kernal,iterations=1)
titles = ['images',"mask","dilation","erosion"]
images = [img,mask,dilation,erosion]
for i in range(len(titles)):
plt.subplot(2,2,i+1)
plt.imshow(images[i],"gray")
plt.title(titles[i])
plt.show() | _____no_output_____ | Apache-2.0 | exercise_2.ipynb | deepak223098/Computer_Vision_Example |
Morphological Transformations using opening morphological operation morphologyEx . Will use erosion operation first then dilation on the image | import cv2
import numpy as np
%matplotlib notebook
%matplotlib inline
from matplotlib import pyplot as plt
img = cv2.imread("hsv_ball.jpg",cv2.IMREAD_GRAYSCALE)
_,mask = cv2.threshold(img, 220,255,cv2.THRESH_BINARY_INV)
kernal = np.ones((5,5),np.uint8)
dilation = cv2.dilate(mask,kernal,iterations = 3)
erosion = cv2.erode(mask,kernal,iterations=1)
opening = cv2.morphologyEx(mask,cv2.MORPH_OPEN,kernal)
titles = ['images',"mask","dilation","erosion","opening"]
images = [img,mask,dilation,erosion,opening]
for i in range(len(titles)):
plt.subplot(2,3,i+1)
plt.imshow(images[i],"gray")
plt.title(titles[i])
plt.show() | _____no_output_____ | Apache-2.0 | exercise_2.ipynb | deepak223098/Computer_Vision_Example |
Morphological Transformations using closing morphological operation morphologyEx . Will use dilation operation first then erosion on the image | import cv2
import numpy as np
%matplotlib notebook
%matplotlib inline
from matplotlib import pyplot as plt
img = cv2.imread("hsv_ball.jpg",cv2.IMREAD_GRAYSCALE)
_,mask = cv2.threshold(img, 220,255,cv2.THRESH_BINARY_INV)
kernal = np.ones((5,5),np.uint8)
dilation = cv2.dilate(mask,kernal,iterations = 3)
erosion = cv2.erode(mask,kernal,iterations=1)
opening = cv2.morphologyEx(mask,cv2.MORPH_OPEN,kernal)
closing = cv2.morphologyEx(mask,cv2.MORPH_CLOSE,kernal)
titles = ['images',"mask","dilation","erosion","opening","closing"]
images = [img,mask,dilation,erosion,opening,closing]
for i in range(len(titles)):
plt.subplot(2,3,i+1)
plt.imshow(images[i],"gray")
plt.title(titles[i])
plt.xticks([])
plt.yticks([])
plt.show() | _____no_output_____ | Apache-2.0 | exercise_2.ipynb | deepak223098/Computer_Vision_Example |
Morphological Transformations other than opening and closing morphological operation MORPH_GRADIENT will give the difference between dilation and erosion top_hat will give the difference between input image and opening image | import cv2
import numpy as np
%matplotlib notebook
%matplotlib inline
from matplotlib import pyplot as plt
img = cv2.imread("hsv_ball.jpg",cv2.IMREAD_GRAYSCALE)
_,mask = cv2.threshold(img, 220,255,cv2.THRESH_BINARY_INV)
kernal = np.ones((5,5),np.uint8)
dilation = cv2.dilate(mask,kernal,iterations = 3)
erosion = cv2.erode(mask,kernal,iterations=1)
opening = cv2.morphologyEx(mask,cv2.MORPH_OPEN,kernal)
closing = cv2.morphologyEx(mask,cv2.MORPH_CLOSE,kernal)
morphlogical_gradient = cv2.morphologyEx(mask,cv2.MORPH_GRADIENT,kernal)
top_hat = cv2.morphologyEx(mask,cv2.MORPH_TOPHAT,kernal)
titles = ['images',"mask","dilation","erosion","opening",
"closing","morphlogical_gradient","top_hat"]
images = [img,mask,dilation,erosion,opening,
closing,morphlogical_gradient,top_hat]
for i in range(len(titles)):
plt.subplot(2,4,i+1)
plt.imshow(images[i],"gray")
plt.title(titles[i])
plt.xticks([])
plt.yticks([])
plt.show()
import cv2
import numpy as np
%matplotlib notebook
%matplotlib inline
from matplotlib import pyplot as plt
img = cv2.imread("HappyFish.jpg",cv2.IMREAD_GRAYSCALE)
_,mask = cv2.threshold(img, 220,255,cv2.THRESH_BINARY_INV)
kernal = np.ones((5,5),np.uint8)
dilation = cv2.dilate(mask,kernal,iterations = 3)
erosion = cv2.erode(mask,kernal,iterations=1)
opening = cv2.morphologyEx(mask,cv2.MORPH_OPEN,kernal)
closing = cv2.morphologyEx(mask,cv2.MORPH_CLOSE,kernal)
MORPH_GRADIENT = cv2.morphologyEx(mask,cv2.MORPH_GRADIENT,kernal)
top_hat = cv2.morphologyEx(mask,cv2.MORPH_TOPHAT,kernal)
titles = ['images',"mask","dilation","erosion","opening",
"closing","MORPH_GRADIENT","top_hat"]
images = [img,mask,dilation,erosion,opening,
closing,MORPH_GRADIENT,top_hat]
for i in range(len(titles)):
plt.subplot(2,4,i+1)
plt.imshow(images[i],"gray")
plt.title(titles[i])
plt.xticks([])
plt.yticks([])
plt.show() | _____no_output_____ | Apache-2.0 | exercise_2.ipynb | deepak223098/Computer_Vision_Example |
Create a list of valid Hindi literals | a = list(set(list("ऀँंःऄअआइईउऊऋऌऍऎएऐऑऒओऔकखगघङचछजझञटठडढणतथदधनऩपफबभमयरऱलळऴवशषसहऺऻ़ऽािीुूृॄॅॆेैॉॊोौ्ॎॏॐ॒॑॓॔ॕॖॗक़ख़ग़ज़ड़ढ़फ़य़ॠॡॢॣ।॥॰ॱॲॳॴॵॶॷॸॹॺॻॼॽॾॿ-")))
len(genderListCleared),len(set(genderListCleared))
genderListCleared = list(set(genderListCleared))
mCount = 0
fCount = 0
nCount = 0
for item in genderListCleared:
if item[1] == 'm':
mCount+=1
elif item[1] == 'f':
fCount+=1
elif item[1] == 'none':
nCount+=1
mCount,fCount,nCount,len(genderListCleared)-mCount-fCount-nCount
with open('genderListCleared', 'wb') as fp:
pickle.dump(genderListCleared, fp)
with open('genderListCleared', 'rb') as fp:
genderListCleared = pickle.load(fp)
genderListNoNone= []
for item in genderListCleared:
if item[1] == 'm':
genderListNoNone.append(item)
elif item[1] == 'f':
genderListNoNone.append(item)
elif item[1] == 'any':
genderListNoNone.append(item)
with open('genderListNoNone', 'wb') as fp:
pickle.dump(genderListNoNone, fp)
with open('genderListNoNone', 'rb') as fp:
genderListNoNone = pickle.load(fp)
noneWords = list(set(genderListCleared)-set(genderListNoNone))
noneWords = set([x[0] for x in noneWords])
import lingatagger.genderlist as gndrlist
import lingatagger.tokenizer as tok
from lingatagger.tagger import *
genders2 = gndrlist.drawlist()
genderList2 = []
for i in genders2:
x = i.split("\t")
if type(numericTagger(x[0])[0]) != tuple:
count = 0
for ch in list(x[0]):
if ch not in a:
count+=1
if count == 0:
if len(x)>=3:
genderList2.append((x[0],'any'))
else:
genderList2.append((x[0],x[1]))
genderList2.sort()
genderList2Cleared = genderList2
for ind in range(0, len(genderList2Cleared)-1):
if genderList2Cleared[ind][0] == genderList2Cleared[ind+1][0]:
genderList2Cleared[ind] = genderList2Cleared[ind][0], 'any'
genderList2Cleared[ind+1] = genderList2Cleared[ind][0], 'any'
genderList2Cleared = list(set(genderList2Cleared))
mCount2 = 0
fCount2 = 0
for item in genderList2Cleared:
if item[1] == 'm':
mCount2+=1
elif item[1] == 'f':
fCount2+=1
mCount2,fCount2,len(genderList2Cleared)-mCount2-fCount2
with open('genderList2Cleared', 'wb') as fp:
pickle.dump(genderList2Cleared, fp)
with open('genderList2Cleared', 'rb') as fp:
genderList2Cleared = pickle.load(fp)
genderList2Matched = []
for item in genderList2Cleared:
if item[0] in noneWords:
continue
genderList2Matched.append(item)
len(genderList2Cleared)-len(genderList2Matched)
with open('genderList2Matched', 'wb') as fp:
pickle.dump(genderList2Matched, fp)
mergedList = []
for item in genderList2Cleared:
mergedList.append((item[0], item[1]))
for item in genderListNoNone:
mergedList.append((item[0], item[1]))
mergedList.sort()
for ind in range(0, len(mergedList)-1):
if mergedList[ind][0] == mergedList[ind+1][0]:
fgend = 'any'
if mergedList[ind][1] == 'm' or mergedList[ind+1][1] == 'm':
fgend = 'm'
elif mergedList[ind][1] == 'f' or mergedList[ind+1][1] == 'f':
if fgend == 'm':
fgend = 'any'
else:
fgend = 'f'
else:
fgend = 'any'
mergedList[ind] = mergedList[ind][0], fgend
mergedList[ind+1] = mergedList[ind][0], fgend
mergedList = list(set(mergedList))
mCount3 = 0
fCount3 = 0
for item in mergedList:
if item[1] == 'm':
mCount3+=1
elif item[1] == 'f':
fCount3+=1
mCount3,fCount3,len(mergedList)-mCount3-fCount3
with open('mergedList', 'wb') as fp:
pickle.dump(mergedList, fp)
with open('mergedList', 'rb') as fp:
mergedList = pickle.load(fp)
np.zeros(18, dtype="int")
from keras.models import Sequential
from keras.layers import Dense, Dropout
from keras.layers import Embedding
from keras.layers import Conv1D, GlobalAveragePooling1D, MaxPooling1D
from keras.layers import Dense, Conv2D, Flatten
from sklearn.feature_extraction.text import CountVectorizer
import numpy as np
import lingatagger.genderlist as gndrlist
import lingatagger.tokenizer as tok
from lingatagger.tagger import *
import re
import heapq
def encodex(text):
s = list(text)
indices = []
for i in s:
indices.append(a.index(i))
encoded = np.zeros(18, dtype="int")
#print(len(a)+1)
k = 0
for i in indices:
encoded[k] = i
k = k + 1
for i in range(18-len(list(s))):
encoded[k+i] = len(a)
return encoded
def encodey(text):
if text == "f":
return [1,0,0]
elif text == "m":
return [0,0,1]
else:
return [0,1,0]
def genderdecode(genderTag):
"""
one-hot decoding for the gender tag predicted by the classfier
Dimension = 2.
"""
genderTag = list(genderTag[0])
index = genderTag.index(heapq.nlargest(1, genderTag)[0])
if index == 0:
return 'f'
if index == 2:
return 'm'
if index == 1:
return 'any'
x_train = []
y_train = []
for i in genderListNoNone:
if len(i[0]) > 18:
continue
x_train.append(encodex(i[0]))
y_train.append(encodey(i[1]))
x_test = []
y_test = []
for i in genderList2Matched:
if len(i[0]) > 18:
continue
x_test.append(encodex(i[0]))
y_test.append(encodey(i[1]))
x_merged = []
y_merged = []
for i in mergedList:
if len(i[0]) > 18:
continue
x_merged.append(encodex(i[0]))
y_merged.append(encodey(i[1]))
X_train = np.array(x_train)
Y_train = np.array(y_train)
X_test = np.array(x_test)
Y_test = np.array(y_test)
X_merged = np.array(x_merged)
Y_merged = np.array(y_merged)
with open('X_train', 'wb') as fp:
pickle.dump(X_train, fp)
with open('Y_train', 'wb') as fp:
pickle.dump(Y_train, fp)
with open('X_test', 'wb') as fp:
pickle.dump(X_test, fp)
with open('Y_test', 'wb') as fp:
pickle.dump(Y_test, fp)
from keras.models import Sequential
from keras.layers import Dense, Dropout
from keras.layers import Embedding
from keras.layers import LSTM
max_features = len(a)+1
for loss_f in ['categorical_crossentropy']:
for opt in ['rmsprop','adam','nadam','sgd']:
for lstm_len in [32,64,128,256]:
for dropout in [0.4,0.45,0.5,0.55,0.6]:
model = Sequential()
model.add(Embedding(max_features, output_dim=18))
model.add(LSTM(lstm_len))
model.add(Dropout(dropout))
model.add(Dense(3, activation='softmax'))
model.compile(loss=loss_f,
optimizer=opt,
metrics=['accuracy'])
print("Training new model, loss:"+loss_f+", optimizer="+opt+", lstm_len="+str(lstm_len)+", dropoff="+str(dropout))
model.fit(X_train, Y_train, batch_size=16, validation_split = 0.2, epochs=10)
score = model.evaluate(X_test, Y_test, batch_size=16)
print("")
print("test score: " + str(score))
print("")
print("") | Training new model, loss:categorical_crossentropy, optimizer=sgd, lstm_len=128, dropoff=0.4
Train on 32318 samples, validate on 8080 samples
Epoch 1/10
32318/32318 [==============================] - 30s 943us/step - loss: 1.0692 - acc: 0.4402 - val_loss: 1.0691 - val_acc: 0.4406
Epoch 2/10
32318/32318 [==============================] - 31s 946us/step - loss: 1.0684 - acc: 0.4407 - val_loss: 1.0690 - val_acc: 0.4406
Epoch 3/10
32318/32318 [==============================] - 31s 944us/step - loss: 1.0684 - acc: 0.4407 - val_loss: 1.0687 - val_acc: 0.4406
Epoch 4/10
32318/32318 [==============================] - 28s 880us/step - loss: 1.0680 - acc: 0.4407 - val_loss: 1.0685 - val_acc: 0.4406
Epoch 5/10
32318/32318 [==============================] - 28s 880us/step - loss: 1.0679 - acc: 0.4407 - val_loss: 1.0676 - val_acc: 0.4406
Epoch 6/10
32318/32318 [==============================] - 30s 933us/step - loss: 1.0671 - acc: 0.4407 - val_loss: 1.0666 - val_acc: 0.4406
Epoch 7/10
32318/32318 [==============================] - 30s 935us/step - loss: 1.0648 - acc: 0.4407 - val_loss: 1.0608 - val_acc: 0.4406
Epoch 8/10
32318/32318 [==============================] - 30s 929us/step - loss: 1.0438 - acc: 0.4623 - val_loss: 1.0237 - val_acc: 0.4759
Epoch 9/10
32318/32318 [==============================] - 30s 930us/step - loss: 0.9995 - acc: 0.4833 - val_loss: 0.9702 - val_acc: 0.5137
Epoch 10/10
32318/32318 [==============================] - 30s 924us/step - loss: 0.9556 - acc: 0.5278 - val_loss: 0.9907 - val_acc: 0.4884
20122/20122 [==============================] - 5s 251us/step
test score: [1.0663544713781388, 0.4062220455341625]
Training new model, loss:categorical_crossentropy, optimizer=sgd, lstm_len=128, dropoff=0.45
Train on 32318 samples, validate on 8080 samples
Epoch 1/10
32318/32318 [==============================] - 35s 1ms/step - loss: 1.0692 - acc: 0.4406 - val_loss: 1.0685 - val_acc: 0.4406
Epoch 2/10
32318/32318 [==============================] - 32s 983us/step - loss: 1.0683 - acc: 0.4407 - val_loss: 1.0684 - val_acc: 0.4406
Epoch 3/10
32318/32318 [==============================] - 30s 934us/step - loss: 1.0684 - acc: 0.4407 - val_loss: 1.0684 - val_acc: 0.4406
Epoch 4/10
32318/32318 [==============================] - 32s 987us/step - loss: 1.0684 - acc: 0.4407 - val_loss: 1.0683 - val_acc: 0.4406
Epoch 5/10
32318/32318 [==============================] - 31s 947us/step - loss: 1.0683 - acc: 0.4407 - val_loss: 1.0685 - val_acc: 0.4406
Epoch 6/10
32318/32318 [==============================] - 31s 944us/step - loss: 1.0678 - acc: 0.4407 - val_loss: 1.0683 - val_acc: 0.4406
Epoch 7/10
32318/32318 [==============================] - 31s 953us/step - loss: 1.0675 - acc: 0.4407 - val_loss: 1.0679 - val_acc: 0.4406
Epoch 8/10
32318/32318 [==============================] - 32s 982us/step - loss: 1.0667 - acc: 0.4407 - val_loss: 1.0663 - val_acc: 0.4406
Epoch 9/10
32318/32318 [==============================] - 31s 949us/step - loss: 1.0625 - acc: 0.4411 - val_loss: 1.0564 - val_acc: 0.4406
Epoch 10/10
32318/32318 [==============================] - 31s 963us/step - loss: 1.0407 - acc: 0.4733 - val_loss: 1.0268 - val_acc: 0.4813
20122/20122 [==============================] - 5s 262us/step
test score: [1.02362715051018, 0.49110426399262525]
Training new model, loss:categorical_crossentropy, optimizer=sgd, lstm_len=128, dropoff=0.5
Train on 32318 samples, validate on 8080 samples
Epoch 1/10
32318/32318 [==============================] - 34s 1ms/step - loss: 1.0695 - acc: 0.4399 - val_loss: 1.0694 - val_acc: 0.4406
Epoch 2/10
32318/32318 [==============================] - 31s 969us/step - loss: 1.0688 - acc: 0.4407 - val_loss: 1.0690 - val_acc: 0.4406
Epoch 3/10
32318/32318 [==============================] - 31s 957us/step - loss: 1.0685 - acc: 0.4407 - val_loss: 1.0686 - val_acc: 0.4406
Epoch 4/10
32318/32318 [==============================] - 32s 986us/step - loss: 1.0684 - acc: 0.4407 - val_loss: 1.0684 - val_acc: 0.4406
Epoch 5/10
32318/32318 [==============================] - 32s 987us/step - loss: 1.0684 - acc: 0.4407 - val_loss: 1.0684 - val_acc: 0.4406
Epoch 6/10
32318/32318 [==============================] - 32s 991us/step - loss: 1.0684 - acc: 0.4407 - val_loss: 1.0683 - val_acc: 0.4406
Epoch 7/10
32318/32318 [==============================] - 31s 963us/step - loss: 1.0683 - acc: 0.4407 - val_loss: 1.0683 - val_acc: 0.4406
Epoch 8/10
32318/32318 [==============================] - 31s 962us/step - loss: 1.0683 - acc: 0.4407 - val_loss: 1.0682 - val_acc: 0.4406
Epoch 9/10
32318/32318 [==============================] - 32s 991us/step - loss: 1.0680 - acc: 0.4407 - val_loss: 1.0678 - val_acc: 0.4406
Epoch 10/10
32318/32318 [==============================] - 33s 1ms/step - loss: 1.0675 - acc: 0.4407 - val_loss: 1.0673 - val_acc: 0.4406
20122/20122 [==============================] - 6s 274us/step
test score: [1.0238210319844738, 0.5285756883043239]
Training new model, loss:categorical_crossentropy, optimizer=sgd, lstm_len=128, dropoff=0.55
Train on 32318 samples, validate on 8080 samples
Epoch 1/10
32318/32318 [==============================] - 35s 1ms/step - loss: 1.0692 - acc: 0.4406 - val_loss: 1.0684 - val_acc: 0.4406
Epoch 2/10
32318/32318 [==============================] - 33s 1ms/step - loss: 1.0687 - acc: 0.4407 - val_loss: 1.0687 - val_acc: 0.4406
Epoch 3/10
32318/32318 [==============================] - 33s 1ms/step - loss: 1.0684 - acc: 0.4407 - val_loss: 1.0682 - val_acc: 0.4406
Epoch 4/10
32318/32318 [==============================] - 32s 991us/step - loss: 1.0683 - acc: 0.4407 - val_loss: 1.0682 - val_acc: 0.4406
Epoch 5/10
32318/32318 [==============================] - 32s 978us/step - loss: 1.0682 - acc: 0.4407 - val_loss: 1.0678 - val_acc: 0.4406
Epoch 6/10
32318/32318 [==============================] - 32s 999us/step - loss: 1.0676 - acc: 0.4407 - val_loss: 1.0689 - val_acc: 0.4406
Epoch 7/10
32318/32318 [==============================] - 32s 999us/step - loss: 1.0672 - acc: 0.4407 - val_loss: 1.0665 - val_acc: 0.4406
Epoch 8/10
32318/32318 [==============================] - 32s 999us/step - loss: 1.0652 - acc: 0.4408 - val_loss: 1.0623 - val_acc: 0.4406
Epoch 9/10
32318/32318 [==============================] - 32s 1ms/step - loss: 1.0509 - acc: 0.4624 - val_loss: 1.0352 - val_acc: 0.4847
Epoch 10/10
32318/32318 [==============================] - 33s 1ms/step - loss: 1.0279 - acc: 0.4883 - val_loss: 1.0159 - val_acc: 0.4948
20122/20122 [==============================] - 6s 300us/step
test score: [1.0234103390857934, 0.49726667329587537]
Training new model, loss:categorical_crossentropy, optimizer=sgd, lstm_len=128, dropoff=0.6
Train on 32318 samples, validate on 8080 samples
Epoch 1/10
32318/32318 [==============================] - 38s 1ms/step - loss: 1.0694 - acc: 0.4406 - val_loss: 1.0685 - val_acc: 0.4406
Epoch 2/10
32318/32318 [==============================] - 33s 1ms/step - loss: 1.0684 - acc: 0.4407 - val_loss: 1.0686 - val_acc: 0.4406
Epoch 3/10
32318/32318 [==============================] - 34s 1ms/step - loss: 1.0685 - acc: 0.4407 - val_loss: 1.0696 - val_acc: 0.4406
Epoch 4/10
32318/32318 [==============================] - 35s 1ms/step - loss: 1.0680 - acc: 0.4407 - val_loss: 1.0685 - val_acc: 0.4406
Epoch 5/10
32318/32318 [==============================] - 34s 1ms/step - loss: 1.0672 - acc: 0.4407 - val_loss: 1.0664 - val_acc: 0.4406
Epoch 6/10
32318/32318 [==============================] - 34s 1ms/step - loss: 1.0639 - acc: 0.4407 - val_loss: 1.0578 - val_acc: 0.4406
Epoch 7/10
32318/32318 [==============================] - 33s 1ms/step - loss: 1.0414 - acc: 0.4698 - val_loss: 1.0244 - val_acc: 0.4806
Epoch 8/10
32318/32318 [==============================] - 33s 1ms/step - loss: 1.0036 - acc: 0.4833 - val_loss: 0.9859 - val_acc: 0.5181
Epoch 9/10
32318/32318 [==============================] - 33s 1ms/step - loss: 0.9609 - acc: 0.5228 - val_loss: 0.9430 - val_acc: 0.5547
Epoch 10/10
32318/32318 [==============================] - 33s 1ms/step - loss: 0.9401 - acc: 0.5384 - val_loss: 0.9377 - val_acc: 0.5335
20122/20122 [==============================] - 6s 285us/step
test score: [1.0087274505276647, 0.5294205347499462]
| Apache-2.0 | Untitled1.ipynb | archit120/lingatagger |
Default server | default_split = split_params(default)[['model','metric','value','params_name','params_val']]
models = default_split.model.unique().tolist()
CollectiveMF_Item_set = default_split[default_split['model'] == models[0]]
CollectiveMF_User_set = default_split[default_split['model'] == models[1]]
CollectiveMF_No_set = default_split[default_split['model'] == models[2]]
CollectiveMF_Both_set = default_split[default_split['model'] == models[3]]
surprise_SVD_set = default_split[default_split['model'] == models[4]]
surprise_Baseline_set = default_split[default_split['model'] == models[5]] | _____no_output_____ | MIT | parse_results_with_visualization/Hyper_params_visualization.ipynb | HenryNebula/Personalization_Final_Project |
surprise_SVD | surprise_SVD_ndcg = surprise_SVD_set[(surprise_SVD_set['metric'] == 'ndcg@10')]
surprise_SVD_ndcg = surprise_SVD_ndcg.pivot(index= 'value',
columns='params_name',
values='params_val').reset_index(inplace = False)
surprise_SVD_ndcg = surprise_SVD_ndcg[surprise_SVD_ndcg.n_factors > 4]
n_factors = [10,50,100,150]
reg_all = [0.01,0.05,0.1,0.5]
lr_all = [0.002,0.005,0.01]
surprise_SVD_ndcg = surprise_SVD_ndcg.sort_values('reg_all')
fig, ax = plt.subplots(1,1, figsize = fig_size)
for i in range(4):
labelstring = 'n_factors = '+ str(n_factors[i])
ax.semilogx('reg_all', 'value',
data = surprise_SVD_ndcg.loc[(surprise_SVD_ndcg['lr_all'] == 0.002)&(surprise_SVD_ndcg['n_factors']== n_factors[i])],
marker= marker[i], markerfacecolor=markerfacecolor[i], markersize=9,
color= color[i], linewidth=3, label = labelstring)
ax.legend()
ax.set_ylabel('ndcg@10',fontsize = 18)
ax.set_xlabel('regParam',fontsize = 18)
ax.set_title('surprise_SVD \n ndcg@10 vs regParam with lr = 0.002',fontsize = 18)
ax.set_xticks(reg_all)
ax.xaxis.set_tick_params(labelsize=14)
ax.yaxis.set_tick_params(labelsize=13)
pic = fig
plt.tight_layout()
pic.savefig('figs/hyper/SVD_ndcg_vs_reg_factor.eps', format='eps')
surprise_SVD_ndcg = surprise_SVD_ndcg.sort_values('n_factors')
fig, ax = plt.subplots(1,1, figsize = fig_size)
for i in range(4):
labelstring = 'regParam = '+ str(reg_all[i])
ax.plot('n_factors', 'value',
data = surprise_SVD_ndcg.loc[(surprise_SVD_ndcg['lr_all'] == 0.002)&(surprise_SVD_ndcg['reg_all']== reg_all[i])],
marker= marker[i], markerfacecolor=markerfacecolor[i], markersize=9,
color= color[i], linewidth=3, label = labelstring)
ax.legend()
ax.set_ylabel('ndcg@10',fontsize = 18)
ax.set_xlabel('n_factors',fontsize = 18)
ax.set_title('surprise_SVD \n ndcg@10 vs n_factors with lr = 0.002',fontsize = 18)
ax.set_xticks(n_factors)
ax.xaxis.set_tick_params(labelsize=14)
ax.yaxis.set_tick_params(labelsize=13)
pic = fig
plt.tight_layout()
pic.savefig('figs/hyper/SVD_ndcg_vs_factor_reg.eps', format='eps') | The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
| MIT | parse_results_with_visualization/Hyper_params_visualization.ipynb | HenryNebula/Personalization_Final_Project |
CollectiveMF_Both | reg_param = [0.0001, 0.001, 0.01]
w_main = [0.5, 0.6, 0.7, 0.8, 0.9, 1.0]
k = [4.,8.,16.]
CollectiveMF_Both_ndcg = CollectiveMF_Both_set[CollectiveMF_Both_set['metric'] == 'ndcg@10']
CollectiveMF_Both_ndcg = CollectiveMF_Both_ndcg.pivot(index= 'value',
columns='params_name',
values='params_val').reset_index(inplace = False)
### Visualization of hyperparameters tuning
fig, ax = plt.subplots(1,1, figsize = fig_size)
CollectiveMF_Both_ndcg.sort_values("reg_param", inplace=True)
for i in range(len(w_main)):
labelstring = 'w_main = '+ str(w_main[i])
ax.semilogx('reg_param', 'value',
data = CollectiveMF_Both_ndcg.loc[(CollectiveMF_Both_ndcg['k'] == 4.0)&(CollectiveMF_Both_ndcg['w_main']== w_main[i])],
marker= marker[i], markerfacecolor= markerfacecolor[i], markersize=9,
color= color[i], linewidth=3, label = labelstring)
ax.legend()
ax.set_ylabel('ndcg@10',fontsize = 18)
ax.set_xlabel('regParam',fontsize = 18)
ax.set_title('CollectiveMF_Both \n ndcg@10 vs regParam with k = 4.0',fontsize = 18)
ax.set_xticks(reg_param)
ax.xaxis.set_tick_params(labelsize=10)
ax.yaxis.set_tick_params(labelsize=13)
pic = fig
plt.tight_layout()
pic.savefig('figs/hyper/CMF_ndcg_vs_reg_w_main.eps', format='eps')
fig, ax = plt.subplots(1,1, figsize = fig_size)
CollectiveMF_Both_ndcg = CollectiveMF_Both_ndcg.sort_values('w_main')
for i in range(len(reg_param)):
labelstring = 'regParam = '+ str(reg_param[i])
ax.plot('w_main', 'value',
data = CollectiveMF_Both_ndcg.loc[(CollectiveMF_Both_ndcg['k'] == 4.0)&(CollectiveMF_Both_ndcg['reg_param']== reg_param[i])],
marker= marker[i], markerfacecolor= markerfacecolor[i], markersize=9,
color= color[i], linewidth=3, label = labelstring)
ax.legend()
ax.set_ylabel('ndcg@10',fontsize = 18)
ax.set_xlabel('w_main',fontsize = 18)
ax.set_title('CollectiveMF_Both \n ndcg@10 vs w_main with k = 4.0',fontsize = 18)
ax.set_xticks(w_main)
ax.xaxis.set_tick_params(labelsize=14)
ax.yaxis.set_tick_params(labelsize=13)
pic = fig
plt.tight_layout()
pic.savefig('figs/hyper/CMF_ndcg_vs_w_main_reg.eps', format='eps') | The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
| MIT | parse_results_with_visualization/Hyper_params_visualization.ipynb | HenryNebula/Personalization_Final_Project |
New server | new_split = split_params(new)[['model','metric','value','params_name','params_val']]
Test_implicit_set = new_split[new_split['model'] == 'BPR']
FMItem_set = new_split[new_split['model'] == 'FMItem']
FMNone_set = new_split[new_split['model'] == 'FMNone'] | _____no_output_____ | MIT | parse_results_with_visualization/Hyper_params_visualization.ipynb | HenryNebula/Personalization_Final_Project |
Test_implicit | Test_implicit_set_ndcg = Test_implicit_set[Test_implicit_set['metric'] == 'ndcg@10']
Test_implicit_set_ndcg = Test_implicit_set_ndcg.pivot(index="value",
columns='params_name',
values='params_val').reset_index(inplace = False)
Test_implicit_set_ndcg = Test_implicit_set_ndcg[Test_implicit_set_ndcg.iteration > 20].copy()
regularization = [0.001,0.005, 0.01 ]
learning_rate = [0.0001, 0.001, 0.005]
factors = [4,8,16]
Test_implicit_set_ndcg.sort_values('regularization', inplace=True)
fig, ax = plt.subplots(1,1, figsize = fig_size)
for i in range(len(factors)):
labelstring = 'n_factors = '+ str(factors[i])
ax.plot('regularization', 'value',
data = Test_implicit_set_ndcg.loc[(Test_implicit_set_ndcg['learning_rate'] == 0.005)&(Test_implicit_set_ndcg['factors']== factors[i])],
marker= marker[i], markerfacecolor=markerfacecolor[i], markersize=9,
color= color[i], linewidth=3, label = labelstring)
ax.legend()
ax.set_ylabel('ndcg@10',fontsize = 18)
ax.set_xlabel('regParam',fontsize = 18)
ax.set_title('BPR \n ndcg@10 vs regParam with lr = 0.005',fontsize = 18)
ax.set_xticks([1e-3, 5e-3, 1e-2])
ax.xaxis.set_tick_params(labelsize=14)
ax.yaxis.set_tick_params(labelsize=13)
pic = fig
plt.tight_layout()
pic.savefig('figs/hyper/BPR_ndcg_vs_reg_factors.eps', format='eps')
Test_implicit_set_ndcg.sort_values('factors', inplace=True)
fig, ax = plt.subplots(1,1, figsize = fig_size)
for i in range(len(regularization)):
labelstring = 'regParam = '+ str(regularization[i])
ax.plot('factors', 'value',
data = Test_implicit_set_ndcg.loc[(Test_implicit_set_ndcg['learning_rate'] == 0.005)&
(Test_implicit_set_ndcg.regularization== regularization[i])],
marker= marker[i], markerfacecolor=markerfacecolor[i], markersize=9,
color= color[i], linewidth=3, label = labelstring)
ax.legend()
ax.set_ylabel('ndcg@10',fontsize = 18)
ax.set_xlabel('n_factors',fontsize = 18)
ax.set_title('BPR \n ndcg@10 vs n_factors with lr = 0.005',fontsize = 18)
ax.set_xticks(factors)
ax.xaxis.set_tick_params(labelsize=14)
ax.yaxis.set_tick_params(labelsize=13)
pic = fig
plt.tight_layout()
pic.savefig('figs/hyper/BPR_ndcg_vs_factors_reg.eps', format='eps',fontsize = 18) | The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
| MIT | parse_results_with_visualization/Hyper_params_visualization.ipynb | HenryNebula/Personalization_Final_Project |
FMItem | FMItem_set_ndcg = FMItem_set[FMItem_set['metric'] == 'ndcg@10']
FMItem_set_ndcg = FMItem_set_ndcg.pivot(index="value",
columns='params_name',
values='params_val').reset_index(inplace = False)
FMItem_set_ndcg = FMItem_set_ndcg[(FMItem_set_ndcg.n_iter == 100) & (FMItem_set_ndcg["rank"] <= 4)].copy()
FMItem_set_ndcg
color = ['lightpink','skyblue','lightgreen', "lightgrey", "navajowhite", "thistle"]
markerfacecolor = ['red', 'blue', 'green','grey', "orangered", "darkviolet" ]
marker = ['P', '^' ,'o', "H", "X", "p"]
reg = [0.2, 0.3, 0.5, 0.8, 0.9, 1]
fct = [2,4]
FMItem_set_ndcg.sort_values('l2_reg_V', inplace=True)
fig, ax = plt.subplots(1,1, figsize = fig_size)
for i in range(len(reg)):
labelstring = 'regParam = '+ str(reg[i])
ax.plot('rank', 'value',
data = FMItem_set_ndcg.loc[(FMItem_set_ndcg.l2_reg_V == reg[i])&
(FMItem_set_ndcg.l2_reg_w == reg[i])],
marker= marker[i], markerfacecolor=markerfacecolor[i], markersize=9,
color= color[i], linewidth=3, label = labelstring)
ax.legend()
ax.set_ylabel('ndcg@10',fontsize = 18)
ax.set_xlabel('n_factors',fontsize = 18)
ax.set_title('FM_Item \n ndcg@10 vs n_factors with lr = 0.005',fontsize = 18)
ax.set_xticks(fct)
ax.xaxis.set_tick_params(labelsize=14)
ax.yaxis.set_tick_params(labelsize=13)
pic = fig
plt.tight_layout()
pic.savefig('figs/hyper/FM_ndcg_vs_factors_reg.eps', format='eps',fontsize = 18)
FMItem_set_ndcg.sort_values('rank', inplace=True)
fig, ax = plt.subplots(1,1, figsize = fig_size)
for i in range(len(fct)):
labelstring = 'n_factors = '+ str(fct[i])
ax.plot('l2_reg_V', 'value',
data = FMItem_set_ndcg.loc[(FMItem_set_ndcg["rank"] == fct[i])],
marker= marker[i], markerfacecolor=markerfacecolor[i], markersize=9,
color= color[i], linewidth=3, label = labelstring)
ax.legend()
ax.set_ylabel('ndcg@10',fontsize = 18)
ax.set_xlabel('regParam',fontsize = 18)
ax.set_title('FM_Item \n ndcg@10 vs n_factors with lr = 0.005',fontsize = 18)
ax.set_xticks(np.arange(0.1, 1.1, 0.1))
ax.xaxis.set_tick_params(labelsize=14)
ax.yaxis.set_tick_params(labelsize=13)
pic = fig
plt.tight_layout()
pic.savefig('figs/hyper/FM_ndcg_vs_reg_factors.eps', format='eps') | The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
| MIT | parse_results_with_visualization/Hyper_params_visualization.ipynb | HenryNebula/Personalization_Final_Project |
Feature Engineering para XGBoost | important_values = values\
.merge(labels, on="building_id")
important_values.drop(columns=["building_id"], inplace = True)
important_values["geo_level_1_id"] = important_values["geo_level_1_id"].astype("category")
important_values
X_train, X_test, y_train, y_test = train_test_split(important_values.drop(columns = 'damage_grade'),
important_values['damage_grade'], test_size = 0.2, random_state = 123)
#OneHotEncoding
def encode_and_bind(original_dataframe, feature_to_encode):
dummies = pd.get_dummies(original_dataframe[[feature_to_encode]])
res = pd.concat([original_dataframe, dummies], axis=1)
res = res.drop([feature_to_encode], axis=1)
return(res)
features_to_encode = ["geo_level_1_id", "land_surface_condition", "foundation_type", "roof_type",\
"position", "ground_floor_type", "other_floor_type",\
"plan_configuration", "legal_ownership_status"]
for feature in features_to_encode:
X_train = encode_and_bind(X_train, feature)
X_test = encode_and_bind(X_test, feature)
X_train
import time
# min_child_weight = [0, 1, 2]
# max_delta_step = [0, 5, 10]
def my_grid_search():
print(time.gmtime())
i = 1
df = pd.DataFrame({'subsample': [],
'gamma': [],
'learning_rate': [],
'max_depth': [],
'score': []})
for subsample in [0.75, 0.885, 0.95]:
for gamma in [0.75, 1, 1.25]:
for learning_rate in [0.4375, 0.45, 0.4625]:
for max_depth in [5, 6, 7]:
model = XGBClassifier(n_estimators = 350,
booster = 'gbtree',
subsample = subsample,
gamma = gamma,
max_depth = max_depth,
learning_rate = learning_rate,
label_encoder = False,
verbosity = 0)
model.fit(X_train, y_train)
y_preds = model.predict(X_test)
score = f1_score(y_test, y_preds, average = 'micro')
df = df.append(pd.Series(
data={'subsample': subsample,
'gamma': gamma,
'learning_rate': learning_rate,
'max_depth': max_depth,
'score': score},
name = i))
print(i, time.gmtime())
i += 1
return df.sort_values('score', ascending = False)
current_df = my_grid_search()
df = pd.read_csv('grid-search/res-feature-engineering.csv')
df.append(current_df)
df.to_csv('grid-search/res-feature-engineering.csv')
current_df
import time
def my_grid_search():
print(time.gmtime())
i = 1
df = pd.DataFrame({'subsample': [],
'gamma': [],
'learning_rate': [],
'max_depth': [],
'score': []})
for subsample in [0.885]:
for gamma in [1]:
for learning_rate in [0.45]:
for max_depth in [5,6,7,8]:
model = XGBClassifier(n_estimators = 350,
booster = 'gbtree',
subsample = subsample,
gamma = gamma,
max_depth = max_depth,
learning_rate = learning_rate,
label_encoder = False,
verbosity = 0)
model.fit(X_train, y_train)
y_preds = model.predict(X_test)
score = f1_score(y_test, y_preds, average = 'micro')
df = df.append(pd.Series(
data={'subsample': subsample,
'gamma': gamma,
'learning_rate': learning_rate,
'max_depth': max_depth,
'score': score},
name = i))
print(i, time.gmtime())
i += 1
return df.sort_values('score', ascending = False)
df = my_grid_search()
# df = pd.read_csv('grid-search/res-feature-engineering.csv')
# df.append(current_df)
df.to_csv('grid-search/res-feature-engineering.csv')
df
pd.read_csv('grid-search/res-no-feature-engineering.csv')\
.nlargest(20, 'score') | _____no_output_____ | MIT | src/VotingClassifier/.ipynb_checkpoints/knn-checkpoint.ipynb | joaquinfontela/Machine-Learning |
Entreno tres de los mejores modelos con Voting. | xgb_model_1 = XGBClassifier(n_estimators = 350,
subsample = 0.885,
booster = 'gbtree',
gamma = 1,
learning_rate = 0.45,
label_encoder = False,
verbosity = 2)
xgb_model_2 = XGBClassifier(n_estimators = 350,
subsample = 0.950,
booster = 'gbtree',
gamma = 0.5,
learning_rate = 0.45,
label_encoder = False,
verbosity = 2)
xgb_model_3 = XGBClassifier(n_estimators = 350,
subsample = 0.750,
booster = 'gbtree',
gamma = 1,
learning_rate = 0.45,
label_encoder = False,
verbosity = 2)
xgb_model_4 = XGBClassifier(n_estimators = 350,
subsample = 0.80,
booster = 'gbtree',
gamma = 1,
learning_rate = 0.55,
label_encoder = False,
verbosity = 2)
rf_model_1 = RandomForestClassifier(n_estimators = 150,
max_depth = None,
max_features = 45,
min_samples_split = 15,
min_samples_leaf = 1,
criterion = "gini",
verbose=True)
rf_model_2 = RandomForestClassifier(n_estimators = 250,
max_depth = None,
max_features = 45,
min_samples_split = 15,
min_samples_leaf = 1,
criterion = "gini",
verbose=True,
n_jobs =-1)
import lightgbm as lgb
lgbm_model_1 = lgb.LGBMClassifier(boosting_type='gbdt',
colsample_bytree=1.0,
importance_type='split',
learning_rate=0.15,
max_depth=None,
n_estimators=1600,
n_jobs=-1,
objective=None,
subsample=1.0,
subsample_for_bin=200000,
subsample_freq=0)
lgbm_model_2 = lgb.LGBMClassifier(boosting_type='gbdt',
colsample_bytree=1.0,
importance_type='split',
learning_rate=0.15,
max_depth=25,
n_estimators=1750,
n_jobs=-1,
objective=None,
subsample=0.7,
subsample_for_bin=240000,
subsample_freq=0)
lgbm_model_3 = lgb.LGBMClassifier(boosting_type='gbdt',
colsample_bytree=1.0,
importance_type='split',
learning_rate=0.20,
max_depth=40,
n_estimators=1450,
n_jobs=-1,
objective=None,
subsample=0.7,
subsample_for_bin=160000,
subsample_freq=0)
import sklearn as sk
import sklearn.neural_network
neuronal_1 = sk.neural_network.MLPClassifier(solver='adam',
activation = 'relu',
learning_rate_init=0.001,
learning_rate = 'adaptive',
verbose=True,
batch_size = 'auto')
gb_model_1 = GradientBoostingClassifier(n_estimators = 305,
max_depth = 9,
min_samples_split = 2,
min_samples_leaf = 3,
subsample=0.6,
verbose=True,
learning_rate=0.15)
vc_model = VotingClassifier(estimators = [('xgb1', xgb_model_1),
('xgb2', xgb_model_2),
('rfm1', rf_model_1),
('lgbm1', lgbm_model_1),
('lgbm2', lgbm_model_2),
('gb_model_1', gb_model_1)],
weights = [1.0, 0.95, 0.85, 1.0, 0.9, 0.7, 0.9],
voting = 'soft',
verbose = True)
vc_model.fit(X_train, y_train)
y_preds = vc_model.predict(X_test)
f1_score(y_test, y_preds, average='micro')
test_values = pd.read_csv('../../csv/test_values.csv', index_col = "building_id")
test_values
test_values_subset = test_values
test_values_subset["geo_level_1_id"] = test_values_subset["geo_level_1_id"].astype("category")
test_values_subset
def encode_and_bind(original_dataframe, feature_to_encode):
dummies = pd.get_dummies(original_dataframe[[feature_to_encode]])
res = pd.concat([original_dataframe, dummies], axis=1)
res = res.drop([feature_to_encode], axis=1)
return(res)
features_to_encode = ["geo_level_1_id", "land_surface_condition", "foundation_type", "roof_type",\
"position", "ground_floor_type", "other_floor_type",\
"plan_configuration", "legal_ownership_status"]
for feature in features_to_encode:
test_values_subset = encode_and_bind(test_values_subset, feature)
test_values_subset
test_values_subset.shape
# Genero las predicciones para los test.
preds = vc_model.predict(test_values_subset)
submission_format = pd.read_csv('../../csv/submission_format.csv', index_col = "building_id")
my_submission = pd.DataFrame(data=preds,
columns=submission_format.columns,
index=submission_format.index)
my_submission.head()
my_submission.to_csv('../../csv/predictions/jf/vote/jf-model-3-submission.csv')
!head ../../csv/predictions/jf/vote/jf-model-3-submission.csv | building_id,damage_grade
300051,3
99355,2
890251,2
745817,1
421793,3
871976,2
691228,1
896100,3
343471,2
| MIT | src/VotingClassifier/.ipynb_checkpoints/knn-checkpoint.ipynb | joaquinfontela/Machine-Learning |
Stock Forecasting using Prophet (Uncertainty in the trend) https://facebook.github.io/prophet/ | # Libraries
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from prophet import Prophet
import warnings
warnings.filterwarnings("ignore")
import yfinance as yf
yf.pdr_override()
stock = 'AMD' # input
start = '2017-01-01' # input
end = '2021-11-08' # input
df = yf.download(stock, start, end)
plt.figure(figsize=(16,8))
plt.plot(df['Adj Close'])
plt.title('Stock Price')
plt.ylabel('Price')
plt.show()
df = df.reset_index()
df = df.rename(columns={'Date': 'ds', 'Close': 'y'})
df
df = df[['ds', 'y']]
df
m = Prophet(daily_seasonality=True)
m.fit(df)
future = m.make_future_dataframe(periods=365)
future.tail()
m = Prophet(mcmc_samples=300)
forecast = m.fit(df).predict(future)
fig = m.plot_components(forecast)
| _____no_output_____ | MIT | Python_Stock/Time_Series_Forecasting/Stock_Forecasting_Prophet_Uncertainty_Trend.ipynb | LastAncientOne/Stock_Analysis_For_Quant |
Delfin InstallationRun the following cell to install osiris-sdk. | !pip install osiris-sdk --upgrade | _____no_output_____ | MIT | delfin/Example - Delfin.ipynb | Open-Dataplatform/examples |
Access to datasetThere are two ways to get access to a dataset1. Service Principle2. Access Token Config file with Service PrincipleIf done with **Service Principle** it is adviced to add the following file with **tenant_id**, **client_id**, and **client_secret**:The structure of **conf.ini**:```[Authorization]tenant_id = client_id = client_secret = [Egress]url = ``` Config file if using Access TokenIf done with **Access Token** then assign it to a variable (see example below).The structure of **conf.ini**:```[Egress]url = ```The egress-url can be [found here](https://github.com/Open-Dataplatform/examples/blob/main/README.md). ImportsExecute the following cell to import the necessary libraries | from osiris.apis.egress import Egress
from osiris.core.azure_client_authorization import ClientAuthorization
from osiris.core.enums import Horizon
from configparser import ConfigParser | _____no_output_____ | MIT | delfin/Example - Delfin.ipynb | Open-Dataplatform/examples |
Initialize the Egress class with Service Principle | config = ConfigParser()
config.read('conf.ini')
client_auth = ClientAuthorization(tenant_id=config['Authorization']['tenant_id'],
client_id=config['Authorization']['client_id'],
client_secret=config['Authorization']['client_secret'])
egress = Egress(client_auth=client_auth,
egress_url=config['Egress']['url']) | _____no_output_____ | MIT | delfin/Example - Delfin.ipynb | Open-Dataplatform/examples |
Intialize the Egress class with Access Token | config = ConfigParser()
config.read('conf.ini')
access_token = 'REPLACE WITH ACCESS TOKEN HERE'
client_auth = ClientAuthorization(access_token=access_token)
egress = Egress(client_auth=client_auth,
egress_url=config['Egress']['url']) | _____no_output_____ | MIT | delfin/Example - Delfin.ipynb | Open-Dataplatform/examples |
Delfin DailyThe data retrived will be **from_date <= data < to_date**.The **from_date** and **to_date** syntax is [described here](https://github.com/Open-Dataplatform/examples/blob/main/README.md). | json_content = egress.download_delfin_file(horizon=Horizon.MINUTELY,
from_date="2021-07-15T20:00",
to_date="2021-07-16T00:00")
json_content = egress.download_delfin_file(horizon=Horizon.DAILY,
from_date="2020-01",
to_date="2020-02")
# We only show the first entry here
json_content[0] | _____no_output_____ | MIT | delfin/Example - Delfin.ipynb | Open-Dataplatform/examples |
Delfin HourlyThe **from_date** and **to_date** syntax is [described here](https://github.com/Open-Dataplatform/examples/blob/main/README.md). | json_content = egress.download_delfin_file(horizon=Horizon.HOURLY,
from_date="2020-01-01T00",
to_date="2020-01-01T06")
# We only show the first entry here
json_content[0] | _____no_output_____ | MIT | delfin/Example - Delfin.ipynb | Open-Dataplatform/examples |
Delfin MinutelyThe **from_date** and **to_date** syntax is [described here](https://github.com/Open-Dataplatform/examples/blob/main/README.md). | json_content = egress.download_delfin_file(horizon=Horizon.MINUTELY,
from_date="2021-07-15T00:00",
to_date="2021-07-15T00:59")
# We only show the first entry here
json_content[0] | _____no_output_____ | MIT | delfin/Example - Delfin.ipynb | Open-Dataplatform/examples |
Delfin Daily with IndicesThe **from_date** and **to_date** syntax is [described here](https://github.com/Open-Dataplatform/examples/blob/main/README.md). | json_content = egress.download_delfin_file(horizon=Horizon.DAILY,
from_date="2020-01-15T03:00",
to_date="2020-01-16T03:01",
table_indices=[1, 2])
# We only show the first entry here
json_content[0] | _____no_output_____ | MIT | delfin/Example - Delfin.ipynb | Open-Dataplatform/examples |
Apple Stock Introduction:We are going to use Apple's stock price. Step 1. Import the necessary libraries | import pandas as pd
import numpy as np
# visualization
import matplotlib.pyplot as plt
%matplotlib inline | _____no_output_____ | BSD-3-Clause | 09_Time_Series/Apple_Stock/Exercises-with-solutions-code.ipynb | nat-bautista/tts-pandas-exercise |
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