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feat: make plot func to plot N_S_comp from csv data

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Skudalen 2021-07-30 10:59:03 +02:00
parent 59dd1b8138
commit a4a5770488
1 changed files with 43 additions and 8 deletions
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51
Neural_Network_Analysis.py
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@ -1,6 +1,7 @@
import json
from keras import callbacks
from pandas.core.frame import DataFrame
from psf_lib.python_speech_features.python_speech_features.base import mfcc
import numpy as np
from sklearn.model_selection import train_test_split
@ -139,7 +140,7 @@ def prepare_datasets_sessions(X, y, session_lengths, test_session_index=4, nr_su
return X_train, X_test, y_train, y_test
# NOT FUNCTIONAL
def prepare_datasets_new(test_session_indexes:list, X, y, session_lengths, nr_subjects=5, nr_sessions=4):
def prepare_datasets_new(test_session_indexes, X, y, session_lengths, nr_subjects=5, nr_sessions=4):
X_list = []
y_list = []
@ -937,6 +938,43 @@ def plot_comp_val_SoftHard(X_soft, y_soft, X_hard, y_hard, session_lengths_soft,
plt.style.use('seaborn-dark-palette')
plt.show()
# Plots training and validation history for CNN_1D network with SOFT and HARD data from CSV file
# Input: None -> CSV from path
# Output: None -> plot & CSV log
def plot_N_S_val_comp():
df_3 = pd.read_csv('/Users/Markus/Prosjekter git/Slovakia 2021/logs/Soft_hard_comparison_3/soft_hard_comparison_acc_data.csv')[['soft_val_acc', 'hard_val_acc']]
df_1 = pd.read_csv('/Users/Markus/Prosjekter git/Slovakia 2021/logs/Soft_hard_comparison_single/soft_hard_comparison_acc_data.csv')[['soft_val_acc', 'hard_val_acc']]
df_3 = df_3.rename(columns={'soft_val_acc': 'natural_val_3', 'hard_val_acc': 'strong_val_3'})
df_1 = df_1.rename(columns={'soft_val_acc': 'natural_val_1', 'hard_val_acc': 'strong_val_1'})
comp_df = pd.concat([df_3, df_1], axis=1)
comp_df.to_csv('logs/Natural_Strong_comp_comb/N_S_val_comp.csv')
# Plot new N/S val comp:
fig, axs = plt.subplots(nrows=1, ncols=2, sharey=True, sharex=True, figsize=(13, 4))
plt.ylim(0, 1)
plt.subplots_adjust(hspace=1.0, top=0.85, bottom=0.15, right=0.75)
fig.text(0.435, 0.03, 'Epochs', ha='center')
fig.text(0.07, 0.5, 'Accuracy', va='center', rotation='vertical')
axs[0].plot(df_3['soft_val_acc'], ':', label='CNN_1D Natural')
axs[0].plot(df_3['hard_val_acc'], '--', label='CNN_1D Strong')
axs[0].set_title('Validation accuracy (3 session training)')
axs[1].plot(df_1['soft_val_acc'], ':', label='CNN_1D Natural')
axs[1].plot(df_1['hard_val_acc'], '--', label='CNN_1D Strong')
axs[1].set_title('Validation accuracy (1 session training)')
#for ax in axs:
# ax.set_xlabel('Epochs')
# ax.set_ylabel('Accuracy')
plt.legend(bbox_to_anchor=(1.75, 0.5), title='Typing behavior evaluated\n', loc='center right')
plt.ylim(0.50, 1.00)
plt.show()
# ----- MODELS ------
# Creates a keras.model with focus on LSTM layers
@ -1006,8 +1044,8 @@ if __name__ == "__main__":
# X.shape = (2806, 1, 208)
# y.shape = (2806, nr_subjects)
# session_lengths.shape = (nr_subjects, nr_sessions)
X_soft, y_soft, session_lengths_soft = load_data_from_json(SOFT_DATA_PATH_MFCC, nr_classes=5)
X_hard, y_hard, session_lengths_hard = load_data_from_json(HARD_DATA_PATH_MFCC, nr_classes=5)
#X_soft, y_soft, session_lengths_soft = load_data_from_json(SOFT_DATA_PATH_MFCC, nr_classes=5)
#X_hard, y_hard, session_lengths_hard = load_data_from_json(HARD_DATA_PATH_MFCC, nr_classes=5)
# Parameters:
NR_SUBJECTS = 5
@ -1129,10 +1167,7 @@ if __name__ == "__main__":
#plot_comp_spread_single(X, y, session_lengths, NR_SESSIONS, epochs=30)
#plot_comp_accuracy_single(X_soft, y_soft, session_lengths_soft, NR_SESSIONS, epochs=30)
plot_comp_val_SoftHard(X_soft, y_soft, X_hard, y_hard, session_lengths_soft, session_lengths_hard, NR_SESSIONS, epochs=30)
#plot_comp_val_SoftHard(X_soft, y_soft, X_hard, y_hard, session_lengths_soft, session_lengths_hard, NR_SESSIONS, epochs=30)
#plot_comp_SoftHard_3(X_soft, y_soft, X_hard, y_hard, session_lengths_soft, session_lengths_hard, NR_SESSIONS, epochs=30)