regModel = LinearRegression(fit_intercept=True,
normalize=False,
copy_X=True,
n_jobs=1)
finRes = []
for i in range(4):
X = np.zeros((len(data[i][0]), 400))
y = np.zeros(len(data[i][0]))
for j in tqdm.trange(len(data[i][0])):
temp = produceWordEmbd(data[i][0][j])
X[j] = temp
y[j] = data[i][1][j]
Res = []
kf = KFold(n_splits=5)
c = CorrelationPearson()
for train_index, test_index in kf.split(X):
X_train, X_test = X[train_index], X[test_index]
y_train, y_test = y[train_index], y[test_index]
model = regModel
model.fit(X_train, y_train)
model_predicted = model.predict(X_test)
Res.append(c.result(y_test, model_predicted))
print(regMethod + "- Pearson Coefficient for " + emotions[i] + ": ",
c.result(y_test, model_predicted))
print(
regMethod + ":Avg of pearson-coefficients for the " + emotions[i] +
" : ",
sum(Res) / 5)
finRes.append(sum(Res) / 5)
model.add(Dense(output_dim = 1, init = 'uniform', activation = 'sigmoid'))
# Compile model
model.compile(loss='mean_squared_error', optimizer='adam')
return model
finRes = []
for i in range(4):
X = np.zeros((len(data[i][0]), 400))
y = np.zeros(len(data[i][0]))
for j in tqdm.trange(len(data[i][0])):
temp = produceWordEmbd(data[i][0][j])
X[j] = temp
y[j] = data[i][1][j]
Res = []
kf = KFold(n_splits=5)
c = CorrelationPearson()
for train_index, test_index in kf.split(X):
X_train, X_test = X[train_index], X[test_index]
y_train, y_test = y[train_index], y[test_index]
model = KerasRegressor(build_fn=NN_model, epochs=100, batch_size=5, verbose=0)
model.fit(X_train, y_train)
model_predicted = model.predict(X_test)
Res.append(c.result(y_test, model_predicted))
print(regMethod +"- Pearson Coefficient for "+ emotions[i] + ": ", c.result(y_test, model_predicted))
print(regMethod + ":Avg of pearson-coefficients for the " + emotions[i] + " : ", sum(Res)/5)
finRes.append(sum(Res)/5)
print("--------------------------------------------")
print("Final PC for "+ regMethod ,sum(finRes)/4)
print("--------------------------------------------")
train_labels[k - sum_arr[i - 1]] = data[i][1][k - sum_arr[i - 1]]
print("Creating dev arrays for " + emotions[j])
for k in tqdm.trange(sum_arr[j - 1], sum_arr[j]):
dev_arrays[k - sum_arr[j - 1]] = model[emotions[j] + str(k)]
dev_labels[k - sum_arr[j - 1]] = data[j][1][k - sum_arr[j - 1]]
print("Training a Neural Network with ")
mlp = MLPRegressor(solver='sgd',
alpha=1e-5,
hidden_layer_sizes=(11, 6, 5),
random_state=1,
activation='relu',
learning_rate='adaptive')
mlp.fit(train_arrays, train_labels)
mlp_predicted = mlp.predict(dev_arrays)
# print(dev_labels - mlp_predicted)
c = CorrelationPearson()
print("pearson-coefficient for " + emotions[i] + ": ",
c.result(dev_labels, mlp_predicted))
regMethods = [
"Neural Nets", "Decision Tree", "Random Forests", "K-NN", "ADA-Boost",
"Gradient-Boost"
]
regModels = [
MLPRegressor(solver='lbfgs',
alpha=1e-5,
hidden_layer_sizes=(9, 5, 7),
random_state=1,
activation='tanh',
learning_rate='adaptive'),
DecisionTreeRegressor(random_state=0),
z[i] = 0
return z
pass
features = [unigram_training_words, bigram_training_words, ngram_training_words, tfidf_training_words]
feature_names = ["unigram", "bigram", "ngram", "tfidf"]
for z in range(6):
for g in range(4):
print("Using feature: "+ feature_names[g] + "*****************************************")
finRes = []
X = features[g]
y = training_labels
y = y.ravel()
Res = []
kf = KFold(n_splits=5)
c = CorrelationPearson()
for train_index, test_index in kf.split(X):
#print("TRAIN:", train_index, "TEST:", test_index)
X_train, X_test = X[train_index], X[test_index]
y_train, y_test = y[train_index], y[test_index]
Rmodel = regModels[z]
Rmodel.fit(X_train, y_train)
Rmodel_predicted = Rmodel.predict(X_test)
"""
y_test_new = get_pos_half(y_test, y_test)
Rmodel_predicted_new = get_pos_half(Rmodel_predicted, y_test)
y_test_new = y_test_new[y_test_new != 0]
Rmodel_predicted_new = Rmodel_predicted_new[Rmodel_predicted_new != 0]
"""
Res.append(c.result(y_test, Rmodel_predicted))
print("Feature used: "+ feature_names[g] +"Regression model: "+regMethods[z] +"---- Pearson Coefficient for "+ emotions[i] + ": ", c.result(y_test, Rmodel_predicted))
import numpy as np
import pandas as pd
from numpy import nan
from sklearn.neighbors import NearestNeighbors
from scipy.sparse import csr_matrix
from sklearn.metrics.pairwise import pairwise_distances
from correlation_pearson.code import CorrelationPearson
pearson = CorrelationPearson()
import operator
from sqlalchemy import create_engine
import pymysql
import mysql.connector
# Database config
mydb = mysql.connector.connect(host="103.129.222.66",
port=3306,
user="******",
password="******",
database="mylearn1_mylearning")
mycursor = mydb.cursor()
db_connection_str = 'mysql+pymysql://mylearn1_mylearn1:W7e3l7:[email protected]:3306/mylearn1_mylearning'
db_connection = create_engine(db_connection_str)
sql = """SELECT us.user_id, us.content_id, c.title, c.content_img, c.description, r.rating, b.bookmarked, t.timespent, us.total_selection
FROM user_selection us
LEFT OUTER JOIN ratings r ON r.user_id = us.user_id AND r.content_id = us.content_id
LEFT OUTER JOIN bookmarks b ON b.user_id = us.user_id AND b.content_id = us.content_id
LEFT OUTER JOIN timespents t ON t.user_id = us.user_id AND t.content_id = us.content_id
#pip install correlation-pearson
from correlation_pearson.code import CorrelationPearson
X_Speed = [0.73, 0.81, 1.53, 1.97, 2.29, 2.86]
X_Energy = [1.507, 1.235, 0.654, 0.864, 0.656, 0.490]
correlation = CorrelationPearson()
print('Correlation coefficient of speed and Energy:' +
str(correlation.result(X_Speed, X_Energy)))
Y_Power = [1.0, 1.0, 1.1, 1.4, 1.5, 1.7]
Y_Energy = [0.654, 1.235, 1.507, 0.490, 0.656, 0.864]
print('Correlation coefficient of Power and Energy:' +
str(correlation.result(Y_Power, Y_Energy)))