We Keep Coding

To forecasting that can predict future data (example x day)

I have Timeseries dataset. I have used cross validation and XGBregressor model. Now i want to forcast my prediction for particular x day.
As per my understaning any fundamental ML prediction model can be expressed as : y = a. f(x)+b. where x = input, b= bias, y= prediction.
I have already trained the model. So for my case x will be time vector. Now the predicted forcasted output matrix say Y already contains all the forcast of x days/month which include everything.
So only thing for me is now to filter out from data what I need.
So, I am trying to write a fucntion argument where Y is the argument and from that i want to filter out for example it say below 20% or what will be forcast on 36 day. Can someone explain me how i can write this?
from sklearn.model_selection import TimeSeriesSplit
import xgboost as xgb
import xgboost as xgbr
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import train_test_split
from sklearn.model_selection import cross_val_score, KFold
from sklearn.metrics import mean_squared_error
import matplotlib.pyplot as plt
from xgboost import XGBRegressor
tss = TimeSeriesSplit(n_splits=3, test_size=24*365*1, gap=24)
df3 = df3.sort_index()
fold = 0
preds = []
scores = []
for train_idx, val_idx in tss.split(df3):
train = df3.iloc[train_idx].dropna()
test = df3.iloc[val_idx].dropna()
FEATURES = ['7day_rolling_avg','Lag_1']
TARGET = 'Liquid Lvl % C'
X_train = train[FEATURES]
y_train = train[TARGET]
X_test = test[FEATURES]
y_test = test[TARGET]
#################################################################################################################
xgbr = xgb.XGBRegressor(verbosity=0)
print(xgbr)
XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1,
colsample_bynode=1, colsample_bytree=1, gamma=0,
importance_type='gain', learning_rate=0.1, max_delta_step=0,
max_depth=3, min_child_weight=1, missing=None, n_estimators=100,
n_jobs=1, nthread=None, objective='reg:linear', random_state=0,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=None,
silent=None, subsample=1, verbosity=1)
xgbr.fit(X_train, y_train)
score = xgbr.score(X_train, y_train)
print("Training score: ", score)
scores = cross_val_score(xgbr, X_train, y_train,cv=3)
print("Mean cross-validation score: %.2f" % scores.mean())
ypred = xgbr.predict(X_test)
mse = mean_squared_error(y_test, ypred)
print("MSE: %.2f" % mse)
print("RMSE: %.2f" % (mse**(1/2.0)))
x_ax = range(len(y_test))
plt.plot(x_ax, y_test, label="original")
plt.plot(x_ax, ypred, label="predicted")
plt.title("Data Prediction")
plt.legend()
plt.show()

Improve precision of my predictive technique in Python

I am using the following Python code to make output predictions depending on some values using decision trees based on entropy/gini index. My input data is contained in the file: https://drive.google.com/file/d/1C8GZ2wiqFUW3WuYxyc0G3axgkM1Uwsb6/view?usp=sharing
The first column "gold" in the file contains the output that I am trying to predict (either T or N). The remaining columns represents some 0 or 1 data that I can use to predict the first column. I am using a test set of 30% and a training set of 70%. I am getting the same precision/recall using either entropy or gini index. I am getting a precision of 0.80 for T and a recall of 0.54 for T. I would like to increase the precision of T and I am okay if the recall for T goes down as well, I am willing to accept this tradeoff. I do not care about the precision/recall of N predictions, I am just trying to improve the precision of T, that's all I care about. I guess increasing the precision means that we should abstain from making predictions in some situations that we are not certain about. How to do that?
# Run this program on your local python
# interpreter, provided you have installed
# the required libraries.
# Importing the required packages
import numpy as np
import pandas as pd
from sklearn.metrics import confusion_matrix
from sklearn.cross_validation import train_test_split
from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import accuracy_score
from sklearn.metrics import classification_report
from sklearn.ensemble import ExtraTreesClassifier
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.feature_selection import SelectKBest
from sklearn.feature_selection import chi2
from sklearn.externals.six import StringIO
from IPython.display import Image
from sklearn.tree import export_graphviz
from sklearn import tree
import collections
import pydotplus
# Function importing Dataset
column_count =0
def importdata():
balance_data = pd.read_csv( 'data1extended.txt', sep= ',')
row_count, column_count = balance_data.shape
# Printing the dataswet shape
print ("Dataset Length: ", len(balance_data))
print ("Dataset Shape: ", balance_data.shape)
print("Number of columns ", column_count)
# Printing the dataset obseravtions
print ("Dataset: ",balance_data.head())
return balance_data, column_count
def columns(balance_data):
row_count, column_count = balance_data.shape
return column_count
# Function to split the dataset
def splitdataset(balance_data, column_count):
# Separating the target variable
X = balance_data.values[:, 1:column_count]
Y = balance_data.values[:, 0]
# Splitting the dataset into train and test
X_train, X_test, y_train, y_test = train_test_split(
X, Y, test_size = 0.3, random_state = 100)
return X, Y, X_train, X_test, y_train, y_test
# Function to perform training with giniIndex.
def train_using_gini(X_train, X_test, y_train):
# Creating the classifier object
clf_gini = DecisionTreeClassifier(criterion = "gini",
random_state = 100,max_depth=3, min_samples_leaf=5)
# Performing training
clf_gini.fit(X_train, y_train)
return clf_gini
# Function to perform training with entropy.
def tarin_using_entropy(X_train, X_test, y_train):
# Decision tree with entropy
clf_entropy = DecisionTreeClassifier(
criterion = "entropy", random_state = 100,
max_depth = 3, min_samples_leaf = 5)
# Performing training
clf_entropy.fit(X_train, y_train)
return clf_entropy
# Function to make predictions
def prediction(X_test, clf_object):
# Predicton on test with giniIndex
y_pred = clf_object.predict(X_test)
print("Predicted values:")
print(y_pred)
return y_pred
# Function to calculate accuracy
def cal_accuracy(y_test, y_pred):
print("Confusion Matrix: ",
confusion_matrix(y_test, y_pred))
print ("Accuracy : ",
accuracy_score(y_test,y_pred)*100)
print("Report : ",
classification_report(y_test, y_pred))
#Univariate selection
def selection(column_count, data):
# data = pd.read_csv("data1extended.txt")
X = data.iloc[:,1:column_count] #independent columns
y = data.iloc[:,0] #target column i.e price range
#apply SelectKBest class to extract top 10 best features
bestfeatures = SelectKBest(score_func=chi2, k=5)
fit = bestfeatures.fit(X,y)
dfscores = pd.DataFrame(fit.scores_)
dfcolumns = pd.DataFrame(X.columns)
df=pd.DataFrame(data, columns=X)
#concat two dataframes for better visualization
featureScores = pd.concat([dfcolumns,dfscores],axis=1)
featureScores.columns = ['Specs','Score'] #naming the dataframe columns
print(featureScores.nlargest(5,'Score')) #print 10 best features
return X,y,data,df
#Feature importance
def feature(X,y):
model = ExtraTreesClassifier()
model.fit(X,y)
print(model.feature_importances_) #use inbuilt class feature_importances of tree based classifiers
#plot graph of feature importances for better visualization
feat_importances = pd.Series(model.feature_importances_, index=X.columns)
feat_importances.nlargest(5).plot(kind='barh')
plt.show()
#Correlation Matrix
def correlation(data, column_count):
corrmat = data.corr()
top_corr_features = corrmat.index
plt.figure(figsize=(column_count,column_count))
#plot heat map
g=sns.heatmap(data[top_corr_features].corr(),annot=True,cmap="RdYlGn")
def generate_decision_tree(X,y):
clf = DecisionTreeClassifier(random_state=0)
data_feature_names = ['callersAtLeast1T','CalleesAtLeast1T','callersAllT','calleesAllT','CallersAtLeast1N','CalleesAtLeast1N','CallersAllN','CalleesAllN','childrenAtLeast1T','parentsAtLeast1T','childrenAtLeast1N','parentsAtLeast1N','childrenAllT','parentsAllT','childrenAllN','ParentsAllN','ParametersatLeast1T','FieldMethodsAtLeast1T','ReturnTypeAtLeast1T','ParametersAtLeast1N','FieldMethodsAtLeast1N','ReturnTypeN','ParametersAllT','FieldMethodsAllT','ParametersAllN','FieldMethodsAllN']
#generate model
model = clf.fit(X, y)
# Create DOT data
dot_data = tree.export_graphviz(clf, out_file=None,
feature_names=data_feature_names,
class_names=y)
# Draw graph
graph = pydotplus.graph_from_dot_data(dot_data)
# Show graph
Image(graph.create_png())
# Create PDF
graph.write_pdf("tree.pdf")
# Create PNG
graph.write_png("tree.png")
# Driver code
def main():
# Building Phase
data,column_count = importdata()
X, Y, X_train, X_test, y_train, y_test = splitdataset(data, column_count)
clf_gini = train_using_gini(X_train, X_test, y_train)
clf_entropy = tarin_using_entropy(X_train, X_test, y_train)
# Operational Phase
print("Results Using Gini Index:")
# Prediction using gini
y_pred_gini = prediction(X_test, clf_gini)
cal_accuracy(y_test, y_pred_gini)
print("Results Using Entropy:")
# Prediction using entropy
y_pred_entropy = prediction(X_test, clf_entropy)
cal_accuracy(y_test, y_pred_entropy)
#COMMENTED OUT THE 4 FOLLOWING LINES DUE TO MEMORY ERROR
#X,y,dataheaders,df=selection(column_count,data)
#generate_decision_tree(X,y)
#feature(X,y)
#correlation(dataheaders,column_count)
# Calling main function
if __name__=="__main__":
main()

I would suggest using Pipelines, to build data pipelines and GridSearchCV to find the best possible hyper-parameters and classifiers for the pipe.
A basic example;
from sklearn.tree import DecisionTreeClassifier
from sklearn.feature_selection import SelectKBest, chi2, f_class
from sklearn.metrics import accuracy_score
from sklearn.pipeline import Pipeline
from sklearn.model_selection import GridSearchCV
pipe = Pipeline[('kbest', SelectKBest(chi2, k=3000)),
('clf', DecisionTreeClassifier())
])
pipe_params = {'kbest__k': range(1, 10, 1),
'kbest__score_func': [f_classif, chi2],
'clf__max_depth': np.arange(1,30),
'clf__min_samples_leaf': [1,2,4,5,10,20,30,40,80,100]}
grid_search = GridSearchCV(pipe, pipe_params, n_jobs=-1
scoring=accuracy_score, cv=10)
grid_search.fit(X_train, Y_train)
This will iterate over every hyper-parameters in pipe_params and choose the best classifier based on accuracy_score.

Adaboosting Ensemble learning

I am implemeting Adaboost algorithm with Decision tree from sklearn library and once i predict the results i got an error any explanation and thank you :
The error is :
AdaBoostClassifier with algorithm='SAMME.R' requires that the weak learner supports the calculation of class probabilities with a predict_proba method.
Please change the base estimator or set algorithm='SAMME' instead.
The dataset is from UCI repository you can access it from this link :
https://archive.ics.uci.edu/ml/machine-learning-databases/heart-disease/
The code is as follow:
from sklearn.model_selection import *
from sklearn.ensemble import AdaBoostClassifier
from sklearn.tree import DecisionTreeClassifier
np.random.seed(1)
selected_features=['sex', 'cp','fbs', 'exang']
X=datascaled[selected_features]
Y=datascaled['num']
# Split the dataset in two equal parts
X_train, X_test, y_train, y_test = train_test_split(
X, Y, test_size=0.3, random_state=0)
param_grid = {"base_estimator__criterion" : ["gini", "entropy"],
"base_estimator__splitter" : ["best", "random"],
"n_estimators": [1, 2]
}
DTC = DecisionTreeClassifier(random_state = 11, max_features = "auto", class_weight = "balanced",max_depth = None)
ABC = AdaBoostClassifier(base_estimator = DTC)
# run grid search
model1=GridSearchCV(ABC, param_grid=param_grid, scoring = 'accuracy')
model1.fit(X_train,y_train)
#The best hyper parameters set
print("Best Hyper Parameters:\n",model1.best_params_)
prediction=model1.predict(X_test)
#importing the metrics module
from sklearn import metrics
#evaluation(Accuracy)
print("Accuracy:",metrics.accuracy_score(prediction,y_test))
#evaluation(Confusion Metrix)
print("Confusion Metrix:\n",metrics.confusion_matrix(prediction,y_test))
#after tuning
import pandas as pd
import numpy as np
from sklearn import preprocessing
import matplotlib.pyplot as plt
plt.rc("font", size=14)
from time import *
from sklearn import metrics
n_folds=10
model=AdaBoostClassifier(random_state = 11,base_estimator= 'entropy', n_estimators= 2)
cv = 10
t0 = time()
y_pred = cross_val_predict(model, X=X, y=Y, n_jobs=-1, cv=cv)
t = time() - t0
print("=" * 52)
print("time cost: {}".format(t))
print()
print("confusion matrix\n", metrics.confusion_matrix(y, y_pred))
print()
print("\t\taccuracy: {}".format(metrics.accuracy_score(y, y_pred)))
print("\t\troc_auc_score: {}".format(metrics.roc_auc_score(y, y_pred)))
print("\t\tcohen_kappa_score: {}".format(metrics.cohen_kappa_score(y, y_pred)))
print()
print("\t\tclassification report")
print("-" * 52)
print(metrics.classification_report(y, y_pred))

roc_auc in VotingClassifier, RandomForestClassifier in scikit-learn (sklearn)

I am trying to calculate roc_auc for hard votingclassifier that i build . i present the code with reprodcible example. now i want to calculate the roc_auc score and plot ROC curver but unfortunately i got the following error predict_proba is not available when voting='hard'
# Voting Ensemble for Classification
import pandas
from sklearn import datasets
from sklearn import model_selection
from sklearn.linear_model import LogisticRegression
from sklearn.tree import DecisionTreeClassifier
from sklearn.svm import SVC
from sklearn.ensemble import VotingClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.naive_bayes import MultinomialNB
from sklearn.cross_validation import StratifiedShuffleSplit
from sklearn.model_selection import cross_val_predict
from sklearn.model_selection import cross_val_score
from sklearn.metrics import make_scorer,confusion_matrix, f1_score, precision_score, recall_score, cohen_kappa_score,accuracy_score,roc_curve
import numpy as np
np.random.seed(42)
iris = datasets.load_iris()
X = iris.data[:, :4] # we only take the first two features.
Y = iris.target
print(Y)
seed = 7
kfold = model_selection.KFold(n_splits=10, random_state=seed)
# create the sub models
estimators = []
model1 = LogisticRegression()
estimators.append(('logistic', model1))
model2 = RandomForestClassifier(n_estimators=200, max_depth=3, random_state=0)
estimators.append(('RandomForest', model2))
model3 = MultinomialNB()
estimators.append(('NaiveBayes', model3))
model4=SVC(probability=True)
estimators.append(('svm', model4))
model5=DecisionTreeClassifier()
estimators.append(('Cart', model5))
# create the ensemble model
print('Majority Class Labels (Majority/Hard Voting)')
ensemble = VotingClassifier(estimators,voting='hard')
#accuracy
results = model_selection.cross_val_score(ensemble, X, Y, cv=kfold,scoring='accuracy')
y_pred = cross_val_predict(ensemble, X ,Y, cv=10)
print("Accuracy ensemble model : %0.2f (+/- %0.2f) " % (results.mean(), results.std() ))
print(results.mean())
#recall
recall_scorer = make_scorer(recall_score, pos_label=1)
recall = cross_val_score(ensemble, X, Y, cv=kfold, scoring=recall_scorer)
print('Recall', np.mean(recall), recall)
# Precision
precision_scorer = make_scorer(precision_score, pos_label=1)
precision = cross_val_score(ensemble, X, Y, cv=kfold, scoring=precision_scorer)
print('Precision', np.mean(precision), precision)
#f1_score
f1_scorer = make_scorer(f1_score, pos_label=1)
f1_score = cross_val_score(ensemble, X, Y, cv=kfold, scoring=f1_scorer)
print('f1_score ', np.mean(f1_score ),f1_score )
#roc_auc_score
roc_auc_score = cross_val_score(ensemble, X, Y, cv=kfold, scoring='roc_auc')
print('roc_auc_score ', np.mean(roc_auc_score ),roc_auc_score )

To calculate the roc_aucmetric you first need to
Replace: ensemble = VotingClassifier(estimators,voting='hard')
with: ensemble = VotingClassifier(estimators,voting='soft').
Next, the last 2 lines of code will throw an error:
roc_auc_score = cross_val_score(ensemble, X, Y, cv=3, scoring='roc_auc')
print('roc_auc_score ', np.mean(roc_auc_score ),roc_auc_score )
ValueError: multiclass format is not supported
This is normal since in Y you have 3 classes (np.unique(Y) == array([0, 1, 2])).
You can't use roc_auc as a single summary metric for multiclass models. If you want, you could calculate **per-class roc_auc.**
How to solve this:
1) Use only two classes to calculate the roc_auc_score
2) use label binarization in advance vefore calling roc_auc_score

Getting Precision and Recall using sklearn

Using the code below, I have the Accuracy . Now I am trying to
1) find the precision and recall for each fold (10 folds total)
2) get the mean for precision
3) get the mean for recall
This could be similar to print(scores) and print("Accuracy: %0.2f (+/- %0.2f)" % (scores.mean(), scores.std() * 2)) below.
Any thoughts?
import numpy as np
from sklearn import cross_validation
from sklearn import datasets
from sklearn import svm
from sklearn.model_selection import StratifiedKFold
iris = datasets.load_iris()
skf = StratifiedKFold(n_splits=10)
clf = svm.SVC(kernel='linear', C=1)
scores = cross_validation.cross_val_score(clf, iris.data, iris.target, cv=10)
print(scores) #[ 1. 0.93333333 1. 1. 0.86666667 1. 0.93333333 1. 1. 1.]
print("Accuracy: %0.2f (+/- %0.2f)" % (scores.mean(), scores.std() * 2)) # Accuracy: 0.97 (+/- 0.09)

This is a bit different, because cross_val_score can't calculate precision/recall for non-binary classification, so you need to use recision_score, recall_score and make cross-validation manually. Parameter average='micro' calculates global precision/recall.
import numpy as np
from sklearn import cross_validation
from sklearn import datasets
from sklearn import svm
from sklearn.model_selection import StratifiedKFold
from sklearn.metrics import precision_score, recall_score
iris = datasets.load_iris()
skf = StratifiedKFold(n_splits=10)
clf = svm.SVC(kernel='linear', C=1)
X = iris.data
y = iris.target
precision_scores = []
recall_scores = []
for train_index, test_index in skf.split(X, y):
X_train, X_test = X[train_index], X[test_index]
y_train, y_test = y[train_index], y[test_index]
y_pred = clf.fit(X_train, y_train).predict(X_test)
precision_scores.append(precision_score(y_test, y_pred, average='micro'))
recall_scores.append(recall_score(y_test, y_pred, average='micro'))
print(precision_scores)
print("Recall: %0.2f (+/- %0.2f)" % (np.mean(precision_scores), np.std(precision_scores) * 2))
print(recall_scores)
print("Recall: %0.2f (+/- %0.2f)" % (np.mean(recall_scores), np.std(recall_scores) * 2))

import pandas as pd
import numpy as np
from sklearn.metrics import confusion_matrix, recall_score, precision_score,
accuracy_score, f1_score,roc_auc_score
def binary_classification_performance(y_test, y_pred):
tp, fp, fn, tn = confusion_matrix(y_test, y_pred).ravel()
accuracy = round(accuracy_score(y_pred = y_pred, y_true = y_test),2)
precision = round(precision_score(y_pred = y_pred, y_true = y_test),2)
recall = round(recall_score(y_pred = y_pred, y_true = y_test),2)
f1_score = round(2*precision*recall/(precision + recall),2)
specificity = round(tn/(tn+fp),2)
npv = round(tn/(tn+fn),2)
auc_roc = round(roc_auc_score(y_score = y_pred, y_true = y_test),2)
result = pd.DataFrame({'Accuracy' : [accuracy],
'Precision (or PPV)' : [precision],
'Recall (senitivity or TPR)' : [recall],
'f1 score' : [f1_score],
'AUC_ROC' : [auc_roc],
'Specificty (or TNR)': [specificity],
'NPV' : [npv],
'True Positive' : [tp],
'True Negative' : [tn],
'False Positive':[fp],
'False Negative':[fn]})
return result
binary_classification_performance(y_test, y_pred)