Problem with samples

[kikomle]

I am getting this error constantly, I tried with test_size instead of train_size but I am getting the same result.

Here is my code: `import csv import datetime import matplotlib.pyplot as plt import numpy as np import os import pandas as pd import seaborn as sns import sklearn.metrics import tensorflow as tf

from numpy import mean from numpy import std from sklearn.model_selection import train_test_split from sklearn.preprocessing import LabelEncoder from tensorflow.keras import activations from tensorflow.keras import layers

from wwo_hist import retrieve_hist_data

BATCH_SIZE = 64 MELTING_TEMPERATURE = 2 MIN_SNOW_CM = 0.5 # Above this value, we consider it as snow NUM_EPOCHS = 20 OUTPUT_DATASET_FILE = "snow_dataset.csv" TFL_MODEL_FILE = "snow_forecast_model.tflite" TFL_MODEL_HEADER_FILE = "snow_forecast_model.h" TF_MODEL = "snow_forecast"

print("data import")

frequency = 1 api_key = '27a946a50c0e4b0daec134825230803' location_list = ['canazei'] df_weather = retrieve_hist_data(api_key, location_list, '01-JAN-2011', '31-DEC-2012', frequency, location_label = False, export_csv = False, store_df = True) t_list = df_weather[0].tempC.astype(float).to_list() h_list = df_weather[0].humidity.astype(float).to_list() s_list = df_weather[0].totalSnow_cm.astype(float).to_list()

print("binarize")

def binarize(snow, threshold): if snow > threshold: return 1 else: return 0

print("graphprint")

s_bin_list = [binarize(snow, 0.5) for snow in s_list]

cm = plt.colormaps.get_cmap('gray_r')

plt.figure(dpi=150)

sc = plt.scatter(t_list, h_list, c=s_bin_list, cmap=cm, label="Snow")

plt.colorbar(sc)

plt.grid(True)

plt.title("Snow(T, H)")

plt.xlabel("Temperature - °C")

plt.ylabel("Humidity - %")

plt.show()

print("labels")

def gen_label(snow, temperature): if snow > 0.5 and temperature < 2: return "Yes" else: return "No"

snow_labels = [gen_label(snow, temp) for snow, temp in zip(s_list, t_list)]

csv_header = ["Temp0", "Temp1", "Temp2", "Humi0", "Humi1", "Humi2", "Snow"] df_dataset = pd.DataFrame(list(zip(t_list[:-2], t_list[1:-1], t_list[:-2], h_list[:-2], h_list[1:-1], h_list[:2], snow_labels[2:])), columns = csv_header)

df0 = df_dataset[df_dataset['Snow'] == "No"] df1 = df_dataset[df_dataset['Snow'] == "Yes"] if len(df1.index) < len(df0.index): df0_sub = df0.sample(len(df1.index)) df_dataset = pd.concat([df0_sub, df1]) else: df1_sub = df1.sample(len(df0.index)) df_dataset = pd.concat([df1_sub, df0])

t_list = df_dataset['Temp0'].tolist() h_list = df_dataset['Humi0'].tolist() t_list = t_list + df_dataset['Temp2'].tail(2).tolist() h_list = t_list + df_dataset['Humi2'].tail(2).tolist()

t_avg = mean(t_list) h_avg = mean(h_list) t_std = std(t_list) h_std = std(h_list)

print("COPY HERE !!!!!") print("Temperature - [MEAN, STD]", round(t_avg, 5), round(t_std, 5)) print("Humidity - [MEAN, STD]", round(h_avg, 5), round(h_std, 5))

def scaling(val, avg, std): return (val - avg) / std;

df_dataset['Temp0'] = df_dataset['Temp0'].apply(lambda x:scaling(x, t_avg, t_std)) df_dataset['Temp1'] = df_dataset['Temp1'].apply(lambda x:scaling(x, t_avg, t_std)) df_dataset['Temp2'] = df_dataset['Temp2'].apply(lambda x:scaling(x, t_avg, t_std))

df_dataset['Humi0'] = df_dataset['Humi0'].apply(lambda x:scaling(x, t_avg, t_std)) df_dataset['Humi1'] = df_dataset['Humi1'].apply(lambda x:scaling(x, t_avg, t_std)) df_dataset['Humi2'] = df_dataset['Humi2'].apply(lambda x:scaling(x, t_avg, t_std))

f_names = df_dataset.columns.values[0:6] l_name = df_dataset.columns x = df_dataset[f_names] y = df_dataset[l_name]

labelencoder = LabelEncoder() labelencoder.fit(y.Snow) y_encoded = labelencoder.transform(y.Snow)

Split 1 (85% vs 15%)

x_train, x_validate_test, y_train, y_validate_test = train_test_split(x, y_encoded, train_size=0.15, random_state = 1)

Split 2 (50% vs 50%)

x_test, x_validate, y_test, y_validate = train_test_split(x_validate_test, y_validate_test, train_size=0.50, random_state = 3)

model = tf.keras.Sequential() model.add(layers.Dense(12, activations='relu', input_shape=(len(f_names),))) model.add(layers.Dropout(0.2)) model.add(layers.Dense(1, activation='sigmoid')) model.summary()

`

[kikomle]

I know it may be a rookie mistake as I have little experience with Python and more with Arduino. I think have included everything but I may be wrong.

[shruthis-shetty]

Hi @kikomle , are we good to close this issue or still need to be solved?

[kikomle]

I resolved that issue but now my code stops after training

[kikomle]

I solved this also by commenting the graph outputs, after those the program stops maybe there is a key to continue?

[zoldaten]

see my comment of second edition of the book