from numpy import where, mean, std, c_, array
from pandas.core.frame import DataFrame
from mlfwk.utils import split_random, get_project_root
from collections import Counter
from mlfwk.models import knn
from mlfwk.metrics import metric
from mlfwk.visualization import generate_space, coloring
from matplotlib.colors import ListedColormap
from mlfwk.readWrite import load_base
from mlfwk.utils import normalization
if __name__ == '__main__':
print("run coluna 3 classes")
# carregar a base
base = load_base(path='column_3C_weka.arff', type='arff')
# features
features = ['pelvic_incidence', 'pelvic_tilt', 'lumbar_lordosis_angle', 'sacral_slope', 'pelvic_radius',
'degree_spondylolisthesis']
print(base.info())
# ----------------------------- Clean the data ----------------------------------------------------------------
# -------------------------- Normalization ------------------------------------------------------------------
# normalizar a base
base[features] = normalization(base[features], type='min-max')
base = base.drop(['pelvic_incidence', 'pelvic_tilt', 'lumbar_lordosis_angle', 'sacral_slope'], axis=1)
'best_cf': [],
'alphas': []
}
results = {
'realization': [],
'ACCURACY': [],
# 'MCC': [],
'f1_score': [],
'precision': [],
'recall': [],
'cf': [],
'alphas': []
}
# carregar a base
base = load_base(path='iris.data', type='csv')
# normalizar a base
base[['SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm', 'PetalWidthCm']] = normalization(
base[['SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm', 'PetalWidthCm']], type='min-max')
N, M = base.shape
C = len(base['Species'].unique())
y_out_of_c = pd.get_dummies(base['Species'])
base = base.drop(['Species'], axis=1)
base = concatenate([base[['SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm', 'PetalWidthCm']], y_out_of_c], axis=1)
for realization in range(20):
train, test = split_random(base, train_percentage=.8)
from pandas import DataFrame
from matplotlib.colors import ListedColormap
from numpy import where, append, ones, array, zeros, mean, argmax, linspace, concatenate, c_, std
from mlfwk.metrics import metric
from mlfwk.readWrite import load_base
from mlfwk.utils import split_random, get_project_root, normalization, out_of_c_to_label
from mlfwk.models import MultiLayerPerceptron
from mlfwk.visualization import generate_space, coloring
if __name__ == '__main__':
print("run emt")
# --------------------------- Read dataset ----------------------------------------
df = load_base('measures_v2.csv', type='csv')
df = df.drop(['profile_id'], axis=1)
df = df.iloc[:100000]
nRow, nCol = df.shape
print(f'There are {nRow} rows and {nCol} columns')
df.info()
features = ['u_q', 'coolant', 'u_d', 'motor_speed',
'i_d', 'i_q', 'ambient', 'torque'] # 'profile_id'
targets = ['stator_yoke', 'pm', 'stator_winding', 'stator_tooth']
# -------------------- Realiztions ---------------------------------------------
'ACCURACY': [],
# 'MCC': [],
'f1_score': [],
'precision': [],
'recall': [],
# 'cf': [],
'alphas': []
}
# carregar a base
columns = []
for i in range(34):
columns.append('x' + str(i))
columns.append('y')
base = load_base(path='dermatology.data', column_names=columns, type='csv')
# features
features = columns[:len(columns) - 1]
print(base.info())
# ----------------------------- Clean the data ----------------------------------------------------------------
# The Age has values ?
for unique_value in base['x33'].unique():
if unique_value != '?':
base['x33'][base['x33'] == unique_value] = int(unique_value)
# ? -> mean of column
base['x33'][base['x33'] == '?'] = int(
np.mean(base['x33'][base['x33'] != '?']))
'R2': [],
'std R2': [],
'alphas': []
}
results = {
'realization': [],
'MSE': [],
'RMSE': [],
'R2': [],
'alphas': []
}
# --------------------------- Read dataset ----------------------------------------
df = load_base('abalone.csv', type='csv')
# The age of abalone is 1.5 + the rings
df['age'] = df.Rings + 1.5
# so after the calculate the age, drop the Rings column
df.drop('Rings', axis=1, inplace=True)
# Label enconding of sex feature
df.Sex = df.Sex.replace({"M": 1, "I": 0, "F": -1})
df.info()
features = [
'Length', 'Diameter', 'Height', 'Whole weight', 'Shucked weight',
'Viscera weight', 'Shell weight', 'Sex'
'alphas': []
}
results = {
'realization': [],
'ACCURACY': [],
# 'MCC': [],
'f1_score': [],
'precision': [],
'recall': [],
# 'cf': [],
'alphas': []
}
# carregar a base
base = load_base(path='breast-cancer-wisconsin.data', type='csv')
base = base.drop(['Sample code number'], axis=1)
# features
features = [
'Clump Thickness', 'Uniformity of Cell Size',
'Uniformity of Cell Shape', 'Marginal Adhesion',
'Single Epithelial Cell Size', 'Bare Nuclei', 'Bland Chromatin',
'Normal Nucleoli', 'Mitoses'
]
print(base.info())
# ----------------------------- Clean the data ----------------------------------------------------------------
# The values at the column Bare Nuclei are all strings so we have to transform to int each of them.
from mlfwk.visualization import generate_space, coloring
if __name__ == '__main__':
print("run car fuel")
final_result = {
'MSE': [],
'std MSE': [],
'RMSE': [],
'std RMSE': [],
'R2': [],
'std R2': []
}
results = {'realization': [], 'MSE': [], 'RMSE': [], 'R2': []}
df = load_base('measurements.csv', type='csv')
# ---------------------------------- cleaning data base --------------------------------------
# NaN Columns
new_df = df.drop(columns=['refill liters', 'refill gas', 'specials'])
# specials_dummies = pd.get_dummies(new_df['specials'])
# change E10 and SP98, for numerical
new_df['gas_type'][new_df['gas_type'] == 'E10'] = int(0)
new_df['gas_type'][new_df['gas_type'] == 'SP98'] = int(1)
new_df['gas_type'] = new_df['gas_type'].astype('int')
target = ['consume']
features = [
