Create AlgorithmsRegression,py
This commit is contained in:
GitHub
parent
a17172db7b
commit
0ee3fb2c8e
196
maths-prog/MachineLearningDemystified/AlgorithmsRegression,py
Normal file
196
maths-prog/MachineLearningDemystified/AlgorithmsRegression,py
Normal file
|
|
@ -0,0 +1,196 @@
|
|||
directory = '/home/nuno/Documents/Jobs/IDInsight'
|
||||
|
||||
import pandas as pd
|
||||
import numpy as np
|
||||
import joblib
|
||||
import matplotlib
|
||||
|
||||
|
||||
## Install the dataframe
|
||||
insuranceDataFrame = pd.read_csv(directory +'insurance_clean_continuous.csv')
|
||||
insuranceDataFrame['charges']
|
||||
## We divide our data into training and test sets, and normalize
|
||||
dfTrain = insuranceDataFrame[:1000]
|
||||
dfTest = insuranceDataFrame[1000:1300]
|
||||
dfCheck = insuranceDataFrame[1300:]
|
||||
|
||||
means = np.mean(dfTrain, axis=0)
|
||||
stds = np.std(dfTrain, axis=0)
|
||||
|
||||
dfTrain = (dfTrain - means) / stds
|
||||
dfTest = (dfTest - means) / stds
|
||||
dfCheck = (dfCheck - means) / stds
|
||||
|
||||
## We don't want this part, only when classifying:
|
||||
## dfTrain['charges'] = (dfTrain['charges']>=0).astype('int')
|
||||
## dfTest['charges'] = (dfTest['charges']>=0).astype('int')
|
||||
## dfCheck['charges'] = (dfCheck['charges']>=0).astype('int')
|
||||
|
||||
## Convert our stuff to arrays
|
||||
trainLabel = np.asarray(dfTrain['charges'])
|
||||
trainData = np.asarray(dfTrain.drop('charges',1))
|
||||
|
||||
testLabel = np.asarray(dfTest['charges'])
|
||||
testData = np.asarray(dfTest.drop('charges',1))
|
||||
|
||||
## We reuse the old function
|
||||
|
||||
def runAlgorithm(Classifier, algorithmName):
|
||||
global scores, algorithms
|
||||
|
||||
Classifier.fit(trainData, trainLabel)
|
||||
scoreInternal = Classifier.score(testData, testLabel)
|
||||
print("score = ", scoreInternal * 100, "/100")
|
||||
|
||||
scores = np.append(scores, scoreInternal) ## These are global scope variables
|
||||
algorithms = np.append(algorithms, algorithmName)
|
||||
|
||||
joblib.dump([Classifier, means, stds],
|
||||
directory +'insuranceModel-' + algorithmName + '.pkl')
|
||||
|
||||
algorithms = []
|
||||
scores = []
|
||||
|
||||
## And we are ready!
|
||||
|
||||
## ******************************************************************
|
||||
|
||||
## Linear Regression
|
||||
algorithmName = "LinearRegression"
|
||||
from sklearn.linear_model import LinearRegression
|
||||
insuranceCheck = LinearRegression()
|
||||
runAlgorithm(insuranceCheck, algorithmName)
|
||||
|
||||
### We also want to visualize some stuff this time:
|
||||
|
||||
coeff = list(insuranceCheck.coef_)
|
||||
labels = list(dfTrain.drop('charges',1).columns)
|
||||
features = pd.DataFrame()
|
||||
features['Features'] = labels
|
||||
features['importance'] = coeff
|
||||
features.sort_values(by=['importance'], ascending=True, inplace=True)
|
||||
features['positive'] = features['importance'] > 0
|
||||
features.set_index('Features', inplace=True)
|
||||
features.importance.plot(kind='barh', figsize=(11, 6),color = features.positive.map({True: 'blue', False: 'red'}))
|
||||
|
||||
## ******************************************************************
|
||||
|
||||
## Lasso
|
||||
algorithmName = "Lasso"
|
||||
from sklearn.linear_model import LassoCV
|
||||
insuranceCheck = LassoCV(cv=5, n_alphas=20, tol=0.0001, n_jobs=-1)
|
||||
runAlgorithm(insuranceCheck, algorithmName)
|
||||
|
||||
## ******************************************************************
|
||||
|
||||
## Nearest Neighbours Regression
|
||||
algorithmName = "NearestNeighboursRegression"
|
||||
|
||||
from sklearn.neighbors import KNeighborsRegressor
|
||||
|
||||
### Let's do some hyperparameter tuning:
|
||||
score=0
|
||||
for n_neighbors in range(1,100):
|
||||
for leaf_size in range(1,100):
|
||||
insuranceCheck = KNeighborsRegressor(n_jobs=-1, n_neighbors=n_neighbors, weights="distance", algorithm="brute", leaf_size=leaf_size, p=2)
|
||||
insuranceCheck.fit(trainData, trainLabel)
|
||||
|
||||
score_temp = insuranceCheck.score(testData, testLabel)
|
||||
|
||||
if(score_temp >score):
|
||||
print("\nscore = ", score_temp * 100, "/100")
|
||||
print("n_neighbors = ", n_neighbors)
|
||||
print("leaf_size = ", leaf_size)
|
||||
score=score_temp
|
||||
|
||||
insuranceCheck = KNeighborsRegressor(n_jobs=-1, n_neighbors=8, weights="distance", algorithm="brute", leaf_size=1, p=2)
|
||||
runAlgorithm(insuranceCheck, algorithmName)
|
||||
|
||||
## ******************************************************************
|
||||
|
||||
## Linear SVR
|
||||
algorithmName = "LinearSVR"
|
||||
from sklearn.svm import LinearSVR
|
||||
|
||||
n=4000
|
||||
insuranceCheck = LinearSVR(max_iter=n)
|
||||
runAlgorithm(insuranceCheck, algorithmName)
|
||||
|
||||
## ******************************************************************
|
||||
|
||||
## SVR different kernels.
|
||||
algorithmName = "SVR_RBF"
|
||||
from sklearn.svm import SVR
|
||||
insuranceCheck = SVR(gamma='auto', C=1, epsilon=0.2, kernel='rbf')
|
||||
## There are different kernels available, including polynomial.
|
||||
## After some tinkering, RBF seems to be the best
|
||||
runAlgorithm(insuranceCheck, algorithmName)
|
||||
|
||||
## ******************************************************************
|
||||
|
||||
## Tree regression.
|
||||
algorithmName = "TreeRegression"
|
||||
from sklearn.tree import DecisionTreeRegressor
|
||||
|
||||
## Hyper parameter optimization.
|
||||
score = 0
|
||||
|
||||
for criterion in np.array(["mse", "friedman_mse", "mae"]):
|
||||
for splitter in np.array(["best", "random"]):
|
||||
for max_depth in np.append(range(1,10), [None]):
|
||||
print("max_depth=",max_depth)
|
||||
for min_samples_split in (np.asarray(range(25))+2):
|
||||
#print("min_samples_split=", min_samples_split)
|
||||
for min_samples_leaf in (np.asarray(range(25))+1):
|
||||
for max_features in np.array(["log2", "auto", None]):
|
||||
insuranceCheck = DecisionTreeRegressor(random_state=0, criterion=criterion, splitter = splitter, max_depth = max_depth, min_samples_split = min_samples_split, min_samples_leaf = min_samples_leaf, max_features = max_features)
|
||||
insuranceCheck.fit(trainData, trainLabel)
|
||||
score_temp = insuranceCheck.score(testData, testLabel)
|
||||
if(score_temp > score):
|
||||
print("score = ", score_temp * 100, "/100")
|
||||
print("\nNEW BEST\ncriterion=", criterion, "\nsplitter =", splitter, "\nmax_depth =", max_depth, "\nmin_samples_split =", min_samples_split, "\nmin_samples_leaf =", min_samples_leaf, "\nmax_features =", max_features)
|
||||
score = score_temp
|
||||
|
||||
insuranceCheck = DecisionTreeRegressor(random_state=0, criterion="mse", splitter = "random", min_samples_split=14, min_samples_leaf=4, max_features = "auto", max_depth= 9)
|
||||
runAlgorithm(insuranceCheck, algorithmName)
|
||||
|
||||
## ******************************************************************
|
||||
|
||||
## Random forest regression.
|
||||
algorithmName = "RandomForestRegression"
|
||||
|
||||
from sklearn.ensemble import RandomForestRegressor
|
||||
insuranceCheck = RandomForestRegressor(n_estimators=500, max_depth=None, random_state=0, n_jobs=-1)
|
||||
runAlgorithm(insuranceCheck, algorithmName)
|
||||
|
||||
## ******************************************************************
|
||||
|
||||
## Extra random forest regression.
|
||||
algorithmName = "ExtraRandomTreesRegression"
|
||||
from sklearn.ensemble import ExtraTreesRegressor
|
||||
insuranceCheck = ExtraTreesRegressor(n_estimators=500, max_depth=None, min_samples_split=20, min_samples_leaf=20, n_jobs=-1)
|
||||
runAlgorithm(insuranceCheck, algorithmName)
|
||||
|
||||
## ******************************************************************
|
||||
## MultiLayerPerceptron Regression(NN)
|
||||
|
||||
from sklearn.neural_network import MLPRegressor
|
||||
algorithmName = "MLP_Regresion"
|
||||
|
||||
for randomstate in range(10):
|
||||
insuranceCheck = MLPRegressor(max_iter=1200, hidden_layer_sizes=(30,30,30), random_state=randomstate, learning_rate="adaptive", activation="relu")
|
||||
insuranceCheck.fit(trainData, trainLabel)
|
||||
score = insuranceCheck.score(testData, testLabel)
|
||||
print("score = ", score * 100, "/100")
|
||||
|
||||
insuranceCheck = MLPRegressor(max_iter=1200, hidden_layer_sizes=(30,30,30), random_state=5, learning_rate="adaptive", activation="relu")
|
||||
runAlgorithm(insuranceCheck, algorithmName)
|
||||
|
||||
## ******************************************************************
|
||||
|
||||
for i in range(len(scores)):
|
||||
print(algorithms[i], ": ", scores[i])
|
||||
|
||||
## And that's it. We notice that most of our algorithms do pretty well, hovering around .81. Dishonorable mention to LinearSVR.
|
||||
|
||||
## Overall, Trees do pretty great.
|
||||
Loading…
Reference in New Issue
Block a user