model.py

#Script to obtain data 
import numpy as np 
import pandas as pd 
import matplotlib.pyplot as plt
import seaborn as sns

from keras.models import Sequential
from keras.layers import Dense
from keras.wrappers.scikit_learn import KerasClassifier
from keras.utils import np_utils
import tensorflow as tf
tf.compat.v1.disable_eager_execution()
tf.compat.v1.disable_v2_behavior()

from sklearn.model_selection import cross_val_score, KFold, train_test_split
from sklearn.preprocessing import LabelEncoder,MinMaxScaler
from sklearn.pipeline import Pipeline
from sklearn.metrics import confusion_matrix, accuracy_score

df = pd.read_csv("data/data_moods.csv")
df.info()
df.head()

col_features = df.columns[6:-3]
X= MinMaxScaler().fit_transform(df[col_features])
X2 = np.array(df[col_features])
Y = df['mood']

encoder = LabelEncoder()
encoder.fit(Y)
encoded_y = encoder.transform(Y)


dummy_y = np_utils.to_categorical(encoded_y)

X_train,X_test,Y_train,Y_test = train_test_split(X,encoded_y,test_size=0.2,random_state=15)

target = pd.DataFrame({'mood':df['mood'].tolist(),'encode':encoded_y}).drop_duplicates().sort_values(['encode'],ascending=True)
target

def base_model():
    model = Sequential()
    model.add(Dense(8,input_dim=10,activation='relu'))
    model.add(Dense(4,activation='softmax'))
    model.compile(loss='categorical_crossentropy',optimizer='adam',
                 metrics=['accuracy'])
    return model
  
estimator = KerasClassifier(build_fn=base_model,epochs=300,batch_size=200,verbose=0)

estimator.fit(X_train,Y_train)
y_preds = estimator.predict(X_test)

cm = confusion_matrix(Y_test,y_preds)
ax = plt.subplot()
sns.heatmap(cm,annot=True,ax=ax)

labels = target['mood']
ax.set_xlabel('Predicted labels')
ax.set_ylabel('True labels')
ax.set_title('Confusion Matrix')
ax.xaxis.set_ticklabels(labels)
ax.yaxis.set_ticklabels(labels)
plt.show()

print("Accuracy Score",accuracy_score(Y_test,y_preds))