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ml_regression.py
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"""
@author: Aayush Agrawal
@Purpose - Re-usable code in Python 3 for cross-validation and regression task in modeling process
"""
## Importing required libraries
import pandas as pd ## For DataFrame operation
import numpy as np ## Numerical python for matrix operations
from sklearn.model_selection import KFold, train_test_split ## Creating cross validation sets
from sklearn import metrics ## For loss functions
import matplotlib.pyplot as plt
## Libraries for Regressiion algorithms
from sklearn.linear_model import LinearRegression
from sklearn.tree import DecisionTreeRegressor
from sklearn.neighbors import KNeighborsRegressor
from sklearn.svm import SVR
import xgboost as xgb
import lightgbm as lgb
from sklearn.ensemble import ExtraTreesRegressor,RandomForestRegressor
import lime
import lime.lime_tabular
########### Cross Validation ###########
### 1) Train test split
def holdout_cv(X,y,size = 0.3, seed = 1):
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = size, random_state = seed)
X_train = X_train.reset_index(drop='index')
X_test = X_test.reset_index(drop='index')
return X_train, X_test, y_train, y_test
### 2) Cross-Validation (K-Fold)
def kfold_cv(X,n_folds = 5, seed = 1):
cv = KFold(n_splits = n_folds, random_state = seed, shuffle = True)
return cv.split(X)
########### Model Explanation ###########
## Variable Importance plot
def feature_importance(model,X):
feature_importance = model.feature_importances_
feature_importance = 100.0 * (feature_importance / feature_importance.max())
sorted_idx = np.argsort(feature_importance)
pos = np.arange(sorted_idx.shape[0]) + .5
plt.figure(figsize=(15, 15))
plt.subplot(1, 2, 2)
plt.barh(pos, feature_importance[sorted_idx], align='center')
plt.yticks(pos, X.columns[sorted_idx])
plt.xlabel('Relative Importance')
plt.title('Variable Importance')
plt.show()
########### Functions for explaination using Lime ###########
## Make a prediction function
def make_prediction_function(model, type = None):
if type == 'xgb':
predict_fn = lambda x: model.predict(xgb.DMatrix(x)).astype(float)
else:
predict_fn = lambda x: model.predict(x).astype(float)
return predict_fn
## Make a lime explainer
def make_lime_explainer(df, c_names = [], verbose_val = True):
explainer = lime.lime_tabular.LimeTabularExplainer(df.values,
class_names=c_names,
feature_names = list(df.columns),
kernel_width=3,
verbose=verbose_val,
mode='regression'
)
return explainer
## Lime explain function
def lime_explain(explainer,predict_fn, df, index = 0, num_features = None,
show_in_notebook = True, filename = None):
if num_features is not None:
exp = explainer.explain_instance(df.values[index], predict_fn, num_features=num_features)
else:
exp = explainer.explain_instance(df.values[index], predict_fn, num_features=df.shape[1])
if show_in_notebook:
exp.show_in_notebook(show_all=False)
if filename is not None:
exp.save_to_file(filename)
########### Algorithms For Regression ###########
### Running Xgboost
def runXGB(train_X, train_y, test_X, test_y=None, test_X2=None, seed_val=0,
rounds=500, dep=8, eta=0.05,sub_sample=0.7,col_sample=0.7,
min_child_weight_val=1, silent_val = 1):
params = {}
params["objective"] = "reg:linear"
params['eval_metric'] = 'rmse'
params["eta"] = eta
params["subsample"] = sub_sample
params["min_child_weight"] = min_child_weight_val
params["colsample_bytree"] = col_sample
params["max_depth"] = dep
params["silent"] = silent_val
params["seed"] = seed_val
#params["max_delta_step"] = 2
#params["gamma"] = 0.5
num_rounds = rounds
plst = list(params.items())
xgtrain = xgb.DMatrix(train_X, label=train_y)
if test_y is not None:
xgtest = xgb.DMatrix(test_X, label=test_y)
watchlist = [ (xgtrain,'train'), (xgtest, 'test') ]
model = xgb.train(plst, xgtrain, num_rounds, watchlist, early_stopping_rounds=100, verbose_eval=20)
else:
xgtest = xgb.DMatrix(test_X)
model = xgb.train(plst, xgtrain, num_rounds)
pred_test_y = model.predict(xgtest, ntree_limit=model.best_iteration)
pred_test_y2 = 0
if test_X2 is not None:
pred_test_y2 = model.predict(xgb.DMatrix(test_X2), ntree_limit=model.best_iteration)
loss = 0
if test_y is not None:
loss = metrics.mean_squared_error(test_y, pred_test_y)
return pred_test_y, loss, pred_test_y2, model
else:
return pred_test_y, loss, pred_test_y2, model
### Running LightGBM
def runLGB(train_X, train_y, test_X, test_y=None, test_X2=None, feature_names=None,
seed_val=0, rounds=500, dep=8, eta=0.05,sub_sample=0.7,
col_sample=0.7,silent_val = 1,min_data_in_leaf_val = 20, bagging_freq = 5):
params = {}
params["objective"] = "regression"
params['metric'] = 'rmse'
params["max_depth"] = dep
params["min_data_in_leaf"] = min_data_in_leaf_val
params["learning_rate"] = eta
params["bagging_fraction"] = sub_sample
params["feature_fraction"] = col_sample
params["bagging_freq"] = bagging_freq
params["bagging_seed"] = seed_val
params["verbosity"] = silent_val
num_rounds = rounds
lgtrain = lgb.Dataset(train_X, label=train_y)
if test_y is not None:
lgtest = lgb.Dataset(test_X, label=test_y)
model = lgb.train(params, lgtrain, num_rounds, valid_sets=[lgtest], early_stopping_rounds=100, verbose_eval=20)
else:
lgtest = lgb.Dataset(test_X)
model = lgb.train(params, lgtrain, num_rounds)
pred_test_y = model.predict(test_X, num_iteration=model.best_iteration)
pred_test_y2 = 0
if test_X2 is not None:
pred_test_y2 = model.predict(test_X2, num_iteration=model.best_iteration)
loss = 0
if test_y is not None:
loss = metrics.mean_squared_error(test_y, pred_test_y)
print(loss)
return pred_test_y, loss, pred_test_y2, model
else:
return pred_test_y, loss, pred_test_y2, model
### Running Extra Trees
def runET(train_X, train_y, test_X, test_y=None, test_X2=None, rounds=100, depth=20,
leaf=10, feat=0.2, min_data_split_val=2,seed_val=0,job = -1):
model = ExtraTreesRegressor(
n_estimators = rounds,
max_depth = depth,
min_samples_split = min_data_split_val,
min_samples_leaf = leaf,
max_features = feat,
n_jobs = job,
random_state = seed_val)
model.fit(train_X, train_y)
train_preds = model.predict(train_X)
test_preds = model.predict(test_X)
test_preds2 = 0
if test_X2 is not None:
test_preds2 = model.predict(test_X2)
test_loss = 0
if test_y is not None:
train_loss = metrics.mean_squared_error(train_y, train_preds)
test_loss = metrics.mean_squared_error(test_y, test_preds)
print("Depth, leaf, feat : ", depth, leaf, feat)
print("Train and Test loss : ", train_loss, test_loss)
return test_preds, test_loss, test_preds2, model
### Running Random Forest
def runRF(train_X, train_y, test_X, test_y=None, test_X2=None, rounds=100, depth=20, leaf=10,
feat=0.2,min_data_split_val=2,seed_val=0,job = -1):
model = RandomForestRegressor(
n_estimators = rounds,
max_depth = depth,
min_samples_split = min_data_split_val,
min_samples_leaf = leaf,
max_features = feat,
n_jobs = job,
random_state = seed_val)
model.fit(train_X, train_y)
train_preds = model.predict(train_X)
test_preds = model.predict(test_X)
test_preds2 = 0
if test_X2 is not None:
test_preds2 = model.predict(test_X2)
test_loss = 0
train_loss = metrics.mean_squared_error(train_y, train_preds)
test_loss = metrics.mean_squared_error(test_y, test_preds)
print("Train and Test loss : ", train_loss, test_loss)
return test_preds, test_loss, test_preds2, model
### Running Linear regression
def runLR(train_X, train_y, test_X, test_y=None, test_X2=None):
model = LinearRegression()
model.fit(train_X, train_y)
train_preds = model.predict(train_X)
test_preds = model.predict(test_X)
test_preds2 = 0
if test_X2 is not None:
test_preds2 = model.predict(test_X2)
test_loss = 0
train_loss = metrics.mean_squared_error(train_y, train_preds)
test_loss = metrics.mean_squared_error(test_y, test_preds)
print("Train and Test loss : ", train_loss, test_loss)
return test_preds, test_loss, test_preds2, model
### Running Decision Tree
def runDT(train_X, train_y, test_X, test_y=None, test_X2=None, criterion='mse',
depth=None, min_split=2, min_leaf=1):
model = DecisionTreeRegressor(
criterion = criterion,
max_depth = depth,
min_samples_split = min_split,
min_samples_leaf=min_leaf)
model.fit(train_X, train_y)
train_preds = model.predict(train_X)
test_preds = model.predict(test_X)
test_preds2 = 0
if test_X2 is not None:
test_preds2 = model.predict(test_X2)
test_loss = 0
train_loss = metrics.mean_squared_error(train_y, train_preds)
test_loss = metrics.mean_squared_error(test_y, test_preds)
print("Train and Test loss : ", train_loss, test_loss)
return test_preds, test_loss, test_preds2, model
### Running K-Nearest Neighbour
def runKNN(train_X, train_y, test_X, test_y=None, test_X2=None,
neighbors=5, job = -1):
model = KNeighborsRegressor(
n_neighbors=neighbors,
n_jobs=job)
model.fit(train_X, train_y)
train_preds = model.predict(train_X)
test_preds = model.predict(test_X)
test_preds2 = 0
if test_X2 is not None:
test_preds2 = model.predict(test_X2)
test_loss = 0
train_loss = metrics.mean_squared_error(train_y, train_preds)
test_loss = metrics.mean_squared_error(test_y, test_preds)
print("Train and Test loss : ", train_loss, test_loss)
return test_preds, test_loss, test_preds2, model
### Running SVM
def runSVC(train_X, train_y, test_X, test_y=None, test_X2=None, C=1.0,
eps=0.1, kernel_choice = 'rbf'):
model = SVR(
C=C,
kernel=kernel_choice,
epsilon=eps)
model.fit(train_X, train_y)
train_preds = model.predict(train_X)
test_preds = model.predict(test_X)
test_preds2 = 0
if test_X2 is not None:
test_preds2 = model.predict(test_X2)
test_loss = 0
train_loss = metrics.mean_squared_error(train_y, train_preds)
test_loss = metrics.mean_squared_error(test_y, test_preds)
print("Train and Test loss : ", train_loss, test_loss)
return test_preds, test_loss, test_preds2, model