def homepage():
st.title("Enter Amazon product url to fetch data")
url = st.text_input("")
if st.button('Fetch Data'):
all_reviews_df, product_title = scraper.scraper(url)
if all_reviews_df is not None:
st.dataframe(all_reviews_df.head())
title = preprocessing.product_name(product_title)
all_reviews_df.to_csv(f"./Amazon Reviews/{title}.csv")
preprocessing.clean_data(all_reviews_df, title)
def predict_all(X, weights, degs, medians, unwanted):
"""
Predicts all labels given raw data.
This means it also separates, cleans and expands the data.
"""
predictions = np.ones((X.shape[0], ))
# for every jet number
for i in range(4):
# get test data
x, ids = data_of_jet_num(X, None, i)
# clean test data
clean_x = clean_data(x, medians[i], unwanted[i])
expanded_x = add_features(clean_x, degs[i])
# predict the labels
p = predict(weights[i], expanded_x)
predictions[ids] = p
return predictions
import matplotlib.pyplot as plt # fix random seed for reproducibility from sklearn.metrics import mean_squared_error from sklearn.preprocessing import MinMaxScaler np.random.seed(7) # Import import pandas as pd import preprocessing as pr data_site_hourly = pd.read_csv(r"data_site/data_1485990000000.csv", low_memory=False) features_hourly = ["Date", pr.PowerPV,pr.Temperature,pr.Irradiation] data_prepared_hourly = pr.prepare_date(data_site_hourly,features_hourly) data_cleaned_hourly = pr.clean_data(data_prepared_hourly) # data = data.reset_index() # Drop date variable data = data_model.copy() data = data.reset_index() data = data.drop(['Date'], 1) # Dimensions of dataset n = data.shape[0] p = data.shape[1] # Make data a np.array
# Learning hyper-parameters
learning_rate = 0.01
n_hidden = 512
total_epoch = 500
n_step = CHUNK # the length of the input sequence
# n_input = n_class = v_len # the size of each input
n_input = 1
n_class = v_len
batch_size = 1
"""
Pre-processing
"""
# Removes unnecessary "NONE" chunks and encodes y values
X_train, y_train = clean_data(training_data, v_map, chunk_size=CHUNK)
number_of_batches = int(math.ceil(len(X_train) / batch_size))
"""
Model
"""
model = Sequential()
model.add(
Conv1D(filters=30,
kernel_size=30,
strides=1,
padding="causal",
input_shape=(512, 1)))
model.add(MaxPool1D(pool_size=2))
model.add(Flatten())
model.add(Reshape((7680, 1)))
def get_pair_plot(df):
df = preprocessing.clean_data(df)
print("plotting")
sns.pairplot(df, hue="cause")
plt.show()
def get_joint_plot(df):
df = preprocessing.clean_data(df)
print("plotting")
sns.jointplot(x="age", y="time", data=df)
plt.show()
def get_count_plot(df):
df = preprocessing.clean_data(df)
print("plotting")
sns.countplot(x="month", hue="race", data=df)
plt.show()
def get_dist_plot(df):
df = preprocessing.clean_data(df)
print("plotting")
sns.distplot(df['time'])
plt.show()
for rect in rects:
height = rect.get_height()
ax.annotate('{}'.format(height),
xy=(rect.get_x() + rect.get_width() / 2, height),
xytext=(0, 3),
textcoords="offset points",
ha='center',
va='bottom')
autolabel(rect)
fig.savefig('plots/Ratings-Distribution.png', facecolor='w')
fig.tight_layout()
plt.show()
##############################################################################
reviews = clean_data(reviews)
discrete_ratings_distribution = reviews[['Rating']].value_counts()
#plot of distribution of ratings
labels = discrete_ratings_distribution.index.get_level_values(0).values
distribution = discrete_ratings_distribution.values
x = np.arange(len(labels))
width = 0.35
fig, ax = plt.subplots(figsize=(12, 6))
rect = ax.bar(x - width / 2,
distribution,
width,
label='Ratings Count',
color='#008891')
).apply(lambda x: float(x[0].replace(',', '.')))
check_timeindex_subscribers(subs)
data_weather_darksky = prw.get_all_weather_darksky()
pr.check_timeindex(data_weather_darksky)
subs_BC = subs.drop_duplicates(['NB BC'], keep='last')
subs_MC = subs.drop_duplicates(['NB MC'], keep='last')
# pr.plot_data(subs_BC,subs_BC.columns,4,0)
data_site_hourly = pr.get_all_data_site()
# FOR BC
features_hourly_BC = ["Date", pr.PowerServiceBC]
data_prepared_hourly_BC = pr.prepare_date(data_site_hourly, features_hourly_BC)
data_cleaned_hourly_BC = pr.clean_data(data_prepared_hourly_BC,
pr.PowerServiceBC, "mean")
pr.check_timeindex(data_cleaned_hourly_BC)
data_merged = data_cleaned_hourly_BC.join(subs['NB BC'], how='outer')
merged_backfill_BC = data_merged.fillna(method='ffill')
merged_backfill_BC.dropna(axis=0, how='any', inplace=True)
pr.plot_data(merged_backfill_BC,
merged_backfill_BC.columns,
"Consomaton vs consumers",
mode=0)
pr.check_timeindex(merged_backfill_BC)
#FOR MC
features_hourly_MC = ["Date", pr.PowerServiceMC]
data_prepared_hourly_MC = pr.prepare_date(data_site_hourly, features_hourly_MC)
data_cleaned_hourly_MC = pr.clean_data(data_prepared_hourly_MC,
pr.PowerServiceMC, "mean")
loaded_model_json = json_file.read()
json_file.close()
model = model_from_json(loaded_model_json)
# load weights into new model
model.load_weights("model.h5")
print("Loaded model from disk")
scaler = joblib.load('scaler_keras_model.save')
print("Loaded scaler from disk")
#Daily Data
data_site_check = pd.read_csv(r"data_site/data_1522997834000.csv",
low_memory=False)
features_check = ["Date", pr.PowerPV]
data_prepared_check = pr.prepare_date(data_site_check, features_check)
data_cleaned_check = pr.clean_data(data_prepared_check)
data = data_cleaned_check.reset_index()
# Drop date variable
data = data.drop(['Date'], 1)
# Make data a np.array
data = data.values
data = scaler.transform(data)
dataX = []
value_start = data[0:1]
obs = value_start
for i in range(0, 12):
obs_reshaped = pd.np.reshape(obs, (obs.shape[0], 1, obs.shape[1]))
