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active_learning_tetr.py
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active_learning_tetr.py
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import pandas as pd
import numpy as np
from matminer.featurizers.conversions import StrToComposition
from matminer.featurizers.composition import ElementProperty
from pymatgen.core import Structure, Composition
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_squared_error, r2_score
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from scipy.stats import gaussian_kde, entropy
import pickle
if __name__ == '__main__':
def shannon_entropy(composition_str):
comp = Composition(composition_str)
frac_comp = comp.fractional_composition.get_el_amt_dict()
entropy = -np.sum([frac * np.log(frac) for frac in frac_comp.values()])
return entropy
# Load the dataset
df = pd.read_csv('df.csv')
# Convert structure strings to pymatgen structures
df['structures_aust'] = df['structures_aust'].map(lambda x: Structure.from_str(x, fmt='json'))
df['structures_mart'] = df['structures_mart'].map(lambda x: Structure.from_str(x, fmt='json'))
# Drop NaN values
df = df.dropna()
# Perform featurization
df = StrToComposition().featurize_dataframe(df, "compositions")
ep_feat = ElementProperty.from_preset(preset_name="magpie")
df = ep_feat.featurize_dataframe(df, col_id="composition")
df['tetragonal_ratio'] = df['structures_mart'].map(lambda x: x.lattice.c / x.lattice.a)
df['shannon_entropy'] = df['compositions'].map(lambda x: shannon_entropy(x))
df['volume'] = df['structures_mart'].map(lambda x: x.lattice.volume)
# Drop unnecessary columns
excluded = ['Unnamed: 0', 'composition', 'compositions', 'structures_aust', 'structures_mart',
'm_aust', 'm_mart', 'e_aust', 'e_mart']
df = df.drop(excluded, axis=1)
# Shuffle the DataFrame
df = df.sample(frac=1)
# Separate features (X) and target variable (y)
y = df['tetragonal_ratio'].values
X = df.drop(columns=['tetragonal_ratio'])
mean_columns = [col for col in df.columns if "mean" in col]
X = X[mean_columns]
# Split dataset into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42)
# Define initial labeled dataset size
start_point = 20
# Initialize labeled dataset
X_active = X_train.iloc[:start_point]
y_active = y_train[:start_point]
# Initialize model
# regression = RandomForestRegressor(n_estimators=200)
regression = RandomForestRegressor(n_estimators=200, criterion='absolute_error')
regression.fit(X_active, y_active)
step = 40
errors = []
consecutive_iteration = 0
previous_mean_absolute_error = 0
consecutive_step = 9
min_step = 200
for n in range(start_point, X_train.shape[0], step):
# Test dataset
X_test_batch = X_train.iloc[n:n + step]
y_test_batch = y_train[n:n + step]
# Make predictions on test dataset
y_pred_test = regression.predict(X_test_batch)
# Calculate absolute error
absolute_error = np.abs(y_pred_test - y_test_batch)
# Calculate mean absolute error
mean_absolute_error = np.mean(absolute_error)
print(n, mean_absolute_error, abs(mean_absolute_error - previous_mean_absolute_error), consecutive_iteration)
errors.append([n, mean_absolute_error])
if abs(mean_absolute_error - previous_mean_absolute_error) < 0.025 and n > min_step:
consecutive_iteration += 1
else:
consecutive_iteration = 0
if consecutive_iteration == consecutive_step:
break
previous_mean_absolute_error = mean_absolute_error
# Select samples
# prediction_variance = np.var(y_pred_test)
# threshold = np.percentile(prediction_variance, 80)
# selected_indices = np.where(prediction_variance >= threshold)[0]
selected_indices = np.where(absolute_error >= 0.025)[0]
# Calculate the entropy of the predicted values
# selected_indices = np.argsort(entropy(y_pred_test / np.sum(y_pred_test, keepdims=True)))[::-1][
# :int(0.2 * len(X_test_batch))] # Selecting top 20% with highest entropy
# Add selected samples to the labeled dataset
X_active = pd.concat([X_active, X_test_batch.iloc[selected_indices]])
y_active = np.hstack([y_active, y_test_batch[selected_indices]])
# Retrain the model with the new labeled samples
regression.fit(X_active, y_active)
# Evaluate the model on the separate testing set
print("size:", X_active.shape[0])
mse_test = mean_squared_error(y_test, regression.predict(X_test))
r2_test = r2_score(y_test, regression.predict(X_test))
print("Test MSE:", mse_test)
print("Test R-squared:", r2_test)
mse_train = mean_squared_error(y_train, regression.predict(X_train))
r2_train = r2_score(y_train, regression.predict(X_train))
print("Train MSE:", mse_train)
print("Train R-squared:", r2_train)
# Plot histogram of predictions
fig = plt.figure()
ax = fig.add_subplot(111)
ax.set_ylabel("Deviation density function", size=28, labelpad=3.0)
ax.set_xlabel("($c/a_{pred} - c/a_{DFT})/c/a_{DFT}$ (%)", size=28, labelpad=3.0)
ax.hist(100 * (regression.predict(X_train) - y_train) / y_train, alpha=0.5, density=True, color="#138A07",
label='Training Set')
ax.hist(100 * (regression.predict(X_test) - y_test) / y_test, alpha=0.5, density=True, color="#bc4749",
label='Testing Set')
# Plot density functions
density_train = gaussian_kde(100 * (regression.predict(X_train) - y_train) / y_train)
density_train.covariance_factor = lambda: .25
density_train._compute_covariance()
xs_train = np.linspace(-25, 25, 200)
ax.plot(xs_train, density_train(xs_train), linewidth=6, color="#138A07")
density_test = gaussian_kde(100 * (regression.predict(X_test) - y_test) / y_test)
density_test.covariance_factor = lambda: .25
density_test._compute_covariance()
xs_test = np.linspace(-25, 25, 200)
ax.plot(xs_test, density_test(xs_test), linewidth=6, color="#bc4749")
ax.axvline(0, linewidth=3, color='black', linestyle='--')
ax.tick_params(axis='both', # Применяем параметры к обеим осям
which='major', # Применяем параметры к вспомогательным делениям
direction='in', # Рисуем деления внутри и снаружи графика
# length = 10, # Длинна делений
# width = 2, # Ширина делений
# color = 'm', # Цвет делений
pad=10, # Расстояние между черточкой и ее подписью
labelsize=24, # Размер подписи
labelcolor='k', # Цвет подписи
bottom=True, # Рисуем метки снизу
top=True, # сверху
left=True, # слева
right=True,
labelbottom=True, # Отображаем подписи снизу
labeltop=False, # сверху нет
labelleft=False, # слева да
labelright=False) # справа нет
legend = ax.legend(fontsize=22,
ncol=1, # количество столбцов
loc='best',
# bbox_to_anchor=(0, -0.05),
facecolor='white', # цвет области
framealpha=1,
# mode="expand",
borderaxespad=0.5,
# edgecolor = 'None', # цвет крайней линии
# title = 'External pressure:', # заголовок
# title_fontsize = 20 # размер шрифта заголовка
)
ax.set_xlim(-27, 27)
fig.set_size_inches(10, 8)
fig.savefig('histogram_tetr_active.png', transparent=False, bbox_inches='tight', dpi=300)
# Plot variation of error with number of iterations
errors = np.array(errors)
fig = plt.figure()
plt.plot(errors[:, 0], errors[:, 1], marker='o', linestyle='-')
a, b = np.polyfit(errors[:, 0], errors[:, 1], 1)
plt.plot(errors[:, 0], a * errors[:, 0] + b, marker='None', linestyle='-', color='orange', linewidth=3)
plt.xlabel('Number of iterations')
plt.ylabel('Mean Absolute Error')
plt.title('Variation of Error with Number of Iterations')
plt.grid(True)
fig.savefig('error_tetr_active.png', transparent=False, bbox_inches='tight', dpi=300)
importances = regression.feature_importances_
# included = np.asarray(included)
included = X.columns.values
indices = np.argsort(importances)[::-1]
fig = plt.figure()
ax = fig.add_subplot(111)
names = [item.replace('MagpieData mean ', '') for item in included[indices][0:10]]
print(importances[indices][0:10] * 100, sum(importances[indices][0:10]))
print(names)
bars = ax.bar(range(len(names)), importances[indices][0:10])
ax.set_xticks([])
ax.set_xticklabels([])
ax.set_yticklabels([int(label * 100) for label in ax.get_yticks()])
ax.set_ylabel(r'Importance (%)', size=14, labelpad=3.0)
ax.set_xlabel(r'Fitch ($x_i$)', size=14, labelpad=3.0)
# Add names to the bars
for i, (bar, name) in enumerate(zip(bars, names)):
height = bar.get_height()
if i < 3:
ax.text(bar.get_x() + bar.get_width() / 2.0, height / 2 - 0.01, name, ha='center', va='bottom',
rotation='vertical', color='white')
else:
ax.text(bar.get_x() + bar.get_width() / 2.0, height + 0.006, name, ha='center', va='bottom',
rotation='vertical', color='black')
ax.tick_params(axis='both', # Apply parameters to both axes
which='major', # Apply parameters to major ticks
direction='in', # Draw ticks inside and outside the plot
pad=10, # Distance between tick and label
labelsize=12, # Label size
labelcolor='k', # Label color
bottom=True, # Draw ticks at the bottom
top=True, # Draw ticks at the top
left=True, # Draw ticks on the left
right=True, # Draw ticks on the right
labelbottom=True, # Display labels at the bottom
labeltop=False, # No labels at the top
labelleft=True, # Display labels on the left
labelright=False) # No labels on the right
fig.savefig('feature_importances_tetr.png', transparent=False, bbox_inches='tight', dpi=300)
print(included[indices][0:10])