Imports¶
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import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
from scipy import stats
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import MinMaxScaler, StandardScaler
from sklearn.metrics import mean_squared_error, confusion_matrix, r2_score, accuracy_score, recall_score, precision_score, classification_report
from sklearn.pipeline import make_pipeline
from keras.models import Sequential
from keras.layers import Dense, Dropout, Embedding, LSTM, GlobalMaxPooling1D, SpatialDropout1D
Load Data¶
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# load data for nasa
data = pd.read_csv('../data/ALL_DATA.csv')
print(data.columns)
print(data.head())
print(np.shape(data))
Scaling (10%)¶
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# *****************************
# HIGHLY IMPORTANT
# *****************************
# Sample data
print("Original Data Stats: \n")
print(data.describe())
print('\n--------\n')
print("New Sample Data Stats: \n")
# all_data['year'].fillna(0).astype(int)
# all_data['mass'].fillna(0).astype(int)
data = data.sample(frac=0.1) # 10% sample set
print(data.describe())
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all_data = pd.DataFrame(data=data)
print(all_data)
# target variable
target = data.year
# features
features = data.drop(['year'], axis=1)
# print(data.head())
features.columns
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One Hot Encoding¶
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from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import OneHotEncoder
# ### Categorical data to be converted to numeric data
# class_data = list(all_data['class'])
# fall_data = list(all_data['fall'])
class_data = list(features['class'])
fall_data = list(features['fall'])
### integer mapping using LabelEncoder
le = LabelEncoder()
class_encoded = le.fit_transform(class_data)
fall_encoded = le.fit_transform(fall_data)
class_encoded = class_encoded.reshape(len(class_encoded), 1)
fall_encoded = fall_encoded.reshape(len(fall_encoded), 1)
### One hot encoding
onehot_encoder = OneHotEncoder(sparse=False)
onehot_encoded_class = onehot_encoder.fit_transform(class_encoded)
onehot_encoded_fall = onehot_encoder.fit_transform(fall_encoded)
# print(onehot_encoded_class)
# all_data['class'] = onehot_encoded_class
# print(all_data['class'])
# print('\n\n\n')
# print(onehot_encoded_fall)
# all_data['fall'] = onehot_encoded_fall
# print(all_data['fall'])
print(onehot_encoded_class)
features['class'] = onehot_encoded_class
print(features['class'])
print('\n\n\n')
print(onehot_encoded_fall)
features['fall'] = onehot_encoded_fall
print(features['fall'])
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# print(all_data.head)
print(features.head)
Scaling¶
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print("Value distribution of features: ")
print(list(features.iloc[0]))
min_max = MinMaxScaler(feature_range = (0, 1))
data_min_max = min_max.fit_transform(features)
print('\n')
print("Value distribution after min max: ")
print(list(data_min_max[0]))
Bar Plot of year¶
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plt.figure(figsize=(10,5))
plt.title('Year')
count = target.value_counts()
print(count)
plt.plot(count)
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Build Model¶
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X_train, X_test, y_train, y_test = train_test_split(features, target, test_size=0.25, random_state=0)
model = Sequential()
model.add(LSTM(50, return_sequences=True))
model.add(LSTM(50, return_sequences=False))
model.add(Dense(25))
model.add(Dense(1))
Compile Model¶
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model.compile(optimizer='adam', loss='mean_squared_error')
Train Model¶
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model.fit(X_train, y_train, batch_size=1, epochs=1)
Model Evaluation¶
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