import os
import random
import numpy as np
import pandas as pd
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler
import plotly.express as px
import plotly.graph_objs as go
from plotly.offline import init_notebook_mode
import requests
# %加载自动重新加载扩展
# %自动重新加载 2
from pytorch_tabular import TabularModel
from pytorch_tabular.models import (
CategoryEmbeddingModelConfig,
GatedAdditiveTreeEnsembleConfig,
MDNConfig
)
from pytorch_tabular.config import (
DataConfig,
OptimizerConfig,
TrainerConfig,
ExperimentConfig,
)
# 来自 pytorch_tabular.categorical_encoders 的 CategoricalEmbeddingTransformer
from pytorch_tabular.models.common.heads import LinearHeadConfig, MixtureDensityHeadConfig
np.random.seed(42)
实用函数¶
def generate_linear_example(samples=int(1e5)):
x_data = np.random.sample(samples)[:, np.newaxis].astype(np.float32)
y_data = np.add(5*x_data, np.multiply((x_data)**2, np.random.standard_normal(x_data.shape)))
x_train, x_valid, y_train, y_valid = train_test_split(x_data, y_data, test_size=0.5, random_state=42)
x_test = np.linspace(0.,1.,int(1e3))[:, np.newaxis].astype(np.float32)
df_train = pd.DataFrame({"col1": x_train.ravel(), "target": y_train.ravel()})
df_valid = pd.DataFrame({"col1": x_valid.ravel(), "target": y_valid.ravel()})
# test = sorted(df_valid.col1.round(3).unique())
# df_test = pd.DataFrame({"col1": test})
df_test = pd.DataFrame({"col1": x_test.ravel()})
return (df_train, df_valid, df_test, ["target"])
def generate_non_linear_example(samples=int(1e5)):
x_data = np.float32(np.random.uniform(-10, 10, (1, samples)))
r_data = np.array([np.random.normal(scale=np.abs(i)) for i in x_data])
y_data = np.float32(np.square(x_data)+r_data*2.0)
x_data2 = np.float32(np.random.uniform(-10, 10, (1, samples)))
r_data2 = np.array([np.random.normal(scale=np.abs(i)) for i in x_data2])
y_data2 = np.float32(-np.square(x_data2)+r_data2*2.0)
x_data = np.concatenate((x_data,x_data2),axis=1).T
y_data = np.concatenate((y_data,y_data2),axis=1).T
min_max_scaler = MinMaxScaler()
y_data = min_max_scaler.fit_transform(y_data)
x_train, x_valid, y_train, y_valid = train_test_split(x_data, y_data, test_size=0.5, random_state=42, shuffle=True)
x_test = np.linspace(-10,10,int(1e3))[:, np.newaxis].astype(np.float32)
df_train = pd.DataFrame({"col1": x_train.ravel(), "target": y_train.ravel()})
df_valid = pd.DataFrame({"col1": x_valid.ravel(), "target": y_valid.ravel()})
# test = sorted(df_valid.col1.round(3).unique())
# df_test = pd.DataFrame({"col1": test})
df_test = pd.DataFrame({"col1": x_test.ravel()})
return (df_train, df_valid, df_test, ["target"])
def generate_step_linear_example(samples=int(1e5)):
x_data = np.random.sample(samples)[:, np.newaxis].astype(np.float32)
y_data = np.zeros(x_data.shape)
mask = x_data<0.5
y_data[mask] = np.add(5*x_data[mask], np.multiply((x_data[mask])**2, np.random.standard_normal(x_data[mask].shape)))
y_data[~mask] = np.add(100*x_data[~mask]+x_data[~mask]**2 , np.multiply((x_data[~mask])**2, np.random.standard_normal(x_data[~mask].shape)))
min_max_scaler = MinMaxScaler()
y_data = min_max_scaler.fit_transform(y_data)
x_train, x_valid, y_train, y_valid = train_test_split(x_data, y_data, test_size=0.5, random_state=42, shuffle=True)
x_test = np.linspace(0.,1.,int(1e3))[:, np.newaxis].astype(np.float32)
df_train = pd.DataFrame({"col1": x_train.ravel(), "target": y_train.ravel()})
df_valid = pd.DataFrame({"col1": x_valid.ravel(), "target": y_valid.ravel()})
# test = sorted(df_valid.col1.round(3).unique())
# df_test = pd.DataFrame({"col1": test})
df_test = pd.DataFrame({"col1": x_test.ravel()})
return (df_train, df_valid, df_test, ["target"])
def generate_gaussian_mixture(samples=int(1e5)):
x_data = np.random.sample(samples)[:, np.newaxis].astype(np.float32)
pi = np.sin(x_data)+3*x_data*np.cos(x_data)
pi = pi/pi.max()
# g1 = np.random.sample(samples) * 4 * x_data.squeeze()
# g2 = np.random.sample(samples) * 15 * x_data.squeeze()
g1 = 2*x_data.squeeze() + 0.5*np.random.sample(samples)
g2 = 8*x_data.squeeze() + 0.5*np.random.sample(samples)
y_data = pi.round().squeeze()*g1 + (1-pi.round().squeeze())*g2
y_data = y_data.reshape(-1,1)
x_train, x_valid, y_train, y_valid = train_test_split(x_data, y_data, test_size=0.5, random_state=42)
x_test = np.linspace(0.,1.,int(1e3))[:, np.newaxis].astype(np.float32)
df_train = pd.DataFrame({"col1": x_train.ravel(), "target": y_train.ravel()})
df_valid = pd.DataFrame({"col1": x_valid.ravel(), "target": y_valid.ravel()})
# test = sorted(df_valid.col1.round(3).unique())
# df_test = pd.DataFrame({"col1": test})
df_test = pd.DataFrame({"col1": x_test.ravel()})
return (df_train, df_valid, df_test, ["target"])
def latex_to_png( formula, file):
tfile = file
r = requests.get( 'http://latex.codecogs.com/png.latex?\dpi{300} \huge %s' % formula )
with open( tfile, 'wb' ) as f:
# f = open( tfile, 'wb' )
f.write( r.content )
# f.close()
线性示例¶
绘图¶
# display(Math(r"$y = 5x + (x^2 * \epsilon)$"+"\n"+r"$\epsilon \backsim \mathcal{N}(0,1)$"))
fig = px.scatter(df_train, x="col1", y="target", title=r"$y = 5x + (x^2 * \epsilon)$"+"\n"+r"$\epsilon \backsim \mathcal{N}(0,1)$")
fig.update_layout(
title={
'y':0.9,
'x':0.5,
'xanchor': 'center',
'yanchor': 'top'})
fig.write_image("imgs/prob_reg_fig_1.png")
训练MDN¶
定义配置¶
epochs = 15
batch_size = 128
steps_per_epoch = int((len(df_train)//batch_size)*0.9)
data_config = DataConfig(
target=['target'],
continuous_cols=['col1'],
categorical_cols=[],
# 连续特征变换="quantile_uniform"
)
trainer_config = TrainerConfig(
auto_lr_find=True, # 运行LRFinder以自动推导学习率
batch_size=batch_size,
max_epochs=epochs,
early_stopping="valid_loss",
early_stopping_patience=5,
checkpoints="valid_loss"
)
optimizer_config = OptimizerConfig(lr_scheduler="ReduceLROnPlateau", lr_scheduler_params={"patience":3})
mdn_head_config = MixtureDensityHeadConfig(num_gaussian=1).__dict__
backbone_config_class = "CategoryEmbeddingModelConfig"
backbone_config = dict(
task="backbone",
layers="128-64", # 每一层的节点数量
activation="ReLU", # 各层之间的激活
initialization="kaiming"
)
model_config = MDNConfig(
task="regression",
backbone_config_class=backbone_config_class,
backbone_config_params=backbone_config,
head_config=mdn_head_config,
learning_rate=1e-3,
)
tabular_model = TabularModel(
data_config=data_config,
model_config=model_config,
optimizer_config=optimizer_config,
trainer_config=trainer_config
)
训练模型¶
预测和可视化¶
fig = go.Figure([
go.Scatter(
name='Mean',
x=pred_df['col1'],
y=pred_df['target_prediction'],
mode='lines',
line=dict(color='rgba(28,53,94,1)'),
),
go.Scatter(
name='Upper Bound',
x=pred_df['col1'],
y=pred_df['target_q75'],
mode='lines',
marker=dict(color='rgba(0,147,201,0.3)'),
line=dict(width=0),
showlegend=False
),
go.Scatter(
name='Lower Bound',
x=pred_df['col1'],
y=pred_df['target_q25'],
marker=dict(color="#444"),
line=dict(width=0),
mode='lines',
fillcolor='rgba(0,147,201,0.3)',
fill='tonexty',
showlegend=False
)
])
fig.update_layout(
yaxis_title='y',
title='Mixture Density Network Prediction',
hovermode="x"
)
# fig.show()
fig.write_image("imgs/prob_reg_mdn_1.png")
非线性示例¶
绘图¶
训练一个前馈神经网络¶
定义配置项¶
epochs = 200
batch_size = 2048
steps_per_epoch = int((len(df_train)//batch_size)*0.9)
data_config = DataConfig(
target=['target'],
continuous_cols=['col1'],
categorical_cols=[],
# continuous_feature_transform="quantile_uniform"
)
trainer_config = TrainerConfig(
auto_lr_find=True, # 运行LRFinder以自动推导学习率。
batch_size=batch_size,
max_epochs=epochs,
early_stopping="valid_loss",
early_stopping_patience=5,
checkpoints="valid_loss"
)
optimizer_config = OptimizerConfig(lr_scheduler="ReduceLROnPlateau", lr_scheduler_params={"patience":3})
model_config = CategoryEmbeddingModelConfig(
task="regression",
layers="16-8", # 每层中的节点数量
activation="ReLU", # 各层之间的激活
head="LinearHead",
learning_rate=1e-3,
)
tabular_model = TabularModel(
data_config=data_config,
model_config=model_config,
optimizer_config=optimizer_config,
trainer_config=trainer_config
)
训练模型¶
预测与可视化¶
fig = go.Figure([
go.Scatter(
name='Prediction',
x=pred_df['col1'],
y=pred_df['target_prediction'],
mode='lines',
line=dict(color='rgba(28,53,94,1)'),
),
go.Scatter(
name='Actual',
x=pred_df['col1'],
y=pred_df['target'],
mode='markers',
line=dict(color='rgba(60,180,229,1)'),
),
])
fig.update_layout(
yaxis_title='y',
title='Category Embedding Prediction',
hovermode="x"
)
# fig.show()
fig.write_image("imgs/prob_reg_non_mdn_2.png")
训练MDN¶
定义配置¶
epochs = 200
batch_size = 2048
steps_per_epoch = int((len(df_train)//batch_size)*0.9)
data_config = DataConfig(
target=['target'],
continuous_cols=['col1'],
categorical_cols=[],
# continuous_feature_transform=连续特征转换"quantile_uniform"
)
trainer_config = TrainerConfig(
auto_lr_find=False, # 运行LRFinder以自动推导学习率
batch_size=batch_size,
max_epochs=epochs,
early_stopping="valid_loss",
early_stopping_patience=5,
checkpoints="valid_loss"
)
optimizer_config = OptimizerConfig(lr_scheduler="ReduceLROnPlateau", lr_scheduler_params={"patience":3})
mdn_head_config = MixtureDensityHeadConfig(num_gaussian=2, weight_regularization=2, lambda_mu=10, lambda_pi=5).__dict__
backbone_config_class = "CategoryEmbeddingModelConfig"
backbone_config = dict(
task="backbone",
layers="128-64", # 每层中的节点数量
activation="ReLU", # 各层之间的激活
head=None,
)
model_config = MDNConfig(
task="regression",
backbone_config_class=backbone_config_class,
backbone_config_params=backbone_config,
head_config=mdn_head_config,
learning_rate=1e-3,
)
tabular_model = TabularModel(
data_config=data_config,
model_config=model_config,
optimizer_config=optimizer_config,
trainer_config=trainer_config
)
训练模型¶
预测与可视化¶
fig = go.Figure([
go.Scatter(
name='Ground Truth',
x=df['col1'],
y=df['target'],
mode='markers',
line=dict(color='rgba(153, 115, 142, 0.2)'),
),
go.Scatter(
name='Component 1',
x=pred_df['col1'],
y=pred_df['mu_0'],
mode='lines',
line=dict(color='rgba(36, 37, 130, 1)'),
),
go.Scatter(
name='Component 2',
x=pred_df['col1'],
y=pred_df['mu_1'],
mode='lines',
line=dict(color='rgba(246, 76, 114, 1)'),
),
go.Scatter(
name='Upper Bound 1',
x=pred_df['col1'],
y=pred_df['mu_0']+pred_df['sigma_0'],
mode='lines',
marker=dict(color='rgba(47, 47, 162, 0.5)'),
# 线宽=字典（宽度=0），
showlegend=False
),
go.Scatter(
name='Lower Bound 1',
x=pred_df['col1'],
y=pred_df['mu_0']-pred_df['sigma_0'],
marker=dict(color="#444"),
line=dict(width=0),
mode='lines',
fillcolor='rgba(47, 47, 162, 0.5)',
fill='tonexty',
showlegend=False
),
go.Scatter(
name='Upper Bound 2',
x=pred_df['col1'],
y=pred_df['mu_1']+pred_df['sigma_1'],
mode='lines',
marker=dict(color='rgba(250, 152, 174, 0.5)'),
# 线宽=字典（宽度=0），
showlegend=False
),
go.Scatter(
name='Lower Bound 2',
x=pred_df['col1'],
y=pred_df['mu_1']-pred_df['sigma_1'],
marker=dict(color="#444"),
line=dict(width=0),
mode='lines',
fillcolor='rgba(250, 152, 174, 0.5)',
fill='tonexty',
showlegend=False
),
])
fig.update_layout(
yaxis_title='y',
title='Mixture Density Network Prediction',
hovermode="x"
)
# 图表展示
fig.write_image("imgs/prob_reg_mdn_2.png")
高斯混合模型¶
绘图¶
eqn = r'$\pi = \frac{sin(x) + 3xcos(x)}{max \left (sin(x) + 3xcos(x) \right )} \\ \\ g1 = 2x + 0.5 \epsilon \rightarrow \epsilon \backsim \mathcal{N}(0,1) \\ g2 = 8x + 0.5 \epsilon \rightarrow \epsilon \backsim \mathcal{N}(0,1) \\ p = Bernoulli(pi) \rightarrow \text{Samples one of two outcomes based on the value of } \pi \\ y = p \times g1 + (1-p) \times g2$'
display(Math(eqn))
fig = px.scatter(df_train, x="col1", y="target")
fig.update_layout(
title={
'y':0.9,
'x':0.5,
'xanchor': 'center',
'yanchor': 'top'})
fig.write_image("imgs/prob_reg_fig_3.png")
训练前馈神经网络¶
定义配置¶
epochs = 200
batch_size = 2048
steps_per_epoch = int((len(df_train)//batch_size)*0.9)
data_config = DataConfig(
target=['target'],
continuous_cols=['col1'],
categorical_cols=[],
# 连续特征变换="quantile_uniform"
)
trainer_config = TrainerConfig(
auto_lr_find=True, # 运行 LRFinder 以自动推导学习率
batch_size=batch_size,
max_epochs=epochs,
early_stopping="valid_loss",
early_stopping_patience=5,
checkpoints="valid_loss"
)
optimizer_config = OptimizerConfig(lr_scheduler="ReduceLROnPlateau", lr_scheduler_params={"patience":3})
model_config = CategoryEmbeddingModelConfig(
task="regression",
layers="16-8", # 每层节点数
activation="ReLU", # 各层之间的激活
head="LinearHead",
learning_rate=1e-3,
)
tabular_model = TabularModel(
data_config=data_config,
model_config=model_config,
optimizer_config=optimizer_config,
trainer_config=trainer_config
)
训练模型¶
预测与可视化¶
fig = go.Figure([
go.Scatter(
name='Prediction',
x=pred_df['col1'],
y=pred_df['target_prediction'],
mode='lines',
line=dict(color='rgba(28,53,94,1)'),
),
go.Scatter(
name='Actual',
x=pred_df['col1'],
y=pred_df['target'],
mode='markers',
line=dict(color='rgba(60,180,229,1)'),
),
])
fig.update_layout(
yaxis_title='y',
title='Category Embedding Network Prediction',
hovermode="x"
)
fig.write_image("imgs/prob_reg_non_mdn_3.png")
训练MDN¶
定义配置¶
epochs = 200
batch_size = 2048
steps_per_epoch = int((len(df_train)//batch_size)*0.9)
data_config = DataConfig(
target=['target'],
continuous_cols=['col1'],
categorical_cols=[],
# 连续特征变换="quantile_uniform"
)
trainer_config = TrainerConfig(
auto_lr_find=True, # 运行LRFinder以自动推导学习率
batch_size=batch_size,
max_epochs=epochs,
early_stopping_patience = 5,
early_stopping="valid_loss",
checkpoints="valid_loss",
load_best=True
)
optimizer_config = OptimizerConfig(lr_scheduler="ReduceLROnPlateau", lr_scheduler_params={"patience":3})
mdn_head_config = MixtureDensityHeadConfig(num_gaussian=2, weight_regularization=2).__dict__
backbone_config_class = "CategoryEmbeddingModelConfig"
backbone_config = dict(
task="backbone",
layers="128-64", # 每一层的节点数量
activation="ReLU", # 各层之间的激活
head=None,
)
model_config = MDNConfig(
task="regression",
backbone_config_class=backbone_config_class,
backbone_config_params=backbone_config,
head_config=mdn_head_config,
learning_rate=1e-3,
)
tabular_model = TabularModel(
data_config=data_config,
model_config=model_config,
optimizer_config=optimizer_config,
trainer_config=trainer_config
)
训练模型¶
预测与可视化¶
fig = go.Figure([
go.Scatter(
name='Ground Truth',
x=df['col1'],
y=df['target'],
mode='markers',
line=dict(color='rgba(153, 115, 142, 0.2)'),
),
go.Scatter(
name='Component 1',
x=pred_df['col1'],
y=pred_df['mu_0'],
mode='lines',
line=dict(color='rgba(90, 92, 237, 1)'),
),
go.Scatter(
name='Component 2',
x=pred_df['col1'],
y=pred_df['mu_1'],
mode='lines',
line=dict(color='rgba(246, 76, 114, 1)'),
),
go.Scatter(
name='Upper Bound 1',
x=pred_df['col1'],
y=pred_df['mu_0']+pred_df['sigma_0'],
mode='lines',
marker=dict(color='rgba(47, 47, 162, 0.5)'),
# 线条宽度=字典（宽度=0），
showlegend=False
),
go.Scatter(
name='Lower Bound 1',
x=pred_df['col1'],
y=pred_df['mu_0']-pred_df['sigma_0'],
marker=dict(color="#444"),
line=dict(width=0),
mode='lines',
fillcolor='rgba(47, 47, 162, 0.5)',
fill='tonexty',
showlegend=False
),
go.Scatter(
name='Upper Bound 2',
x=pred_df['col1'],
y=pred_df['mu_1']+pred_df['sigma_1'],
mode='lines',
marker=dict(color='rgba(250, 152, 174, 0.5)'),
# 线条宽度=字典（宽度=0），
showlegend=False
),
go.Scatter(
name='Lower Bound 2',
x=pred_df['col1'],
y=pred_df['mu_1']-pred_df['sigma_1'],
marker=dict(color="#444"),
line=dict(width=0),
mode='lines',
fillcolor='rgba(250, 152, 174, 0.5)',
fill='tonexty',
showlegend=False
),
])
fig.update_layout(
yaxis_title='y',
# y轴范围=[0, 1],
title='Mixture Density Network Prediction',
hovermode="x",
yaxis_range=[df['target'].min()*0.85, df['target'].max()*1.15]
)
fig.write_image("imgs/prob_reg_mdn_3.png")
fig = go.Figure([
go.Scatter(
name='Ground Truth',
x=df['col1'],
y=df['target'],
mode='markers',
line=dict(color='rgba(153, 115, 142, 0.2)'),
),
go.Scatter(
name='Component 1',
x=pred_df['col1'],
y=pred_df['mu_0'],
mode='lines',
line=dict(color='rgba(90, 92, 237, 1)'),
),
go.Scatter(
name='Mixing Coefficient 1',
x=pred_df['col1'],
y=pred_df['pi_1'],
mode='lines',
line=dict(color='rgba(255, 216, 117, 1)'),
),
go.Scatter(
name='Upper Bound 1',
x=pred_df['col1'],
y=pred_df['mu_0']+pred_df['sigma_0'],
mode='lines',
marker=dict(color='rgba(47, 47, 162, 0.5)'),
# 线条宽度=0，
showlegend=False
),
go.Scatter(
name='Lower Bound 1',
x=pred_df['col1'],
y=pred_df['mu_0']-pred_df['sigma_0'],
marker=dict(color="#444"),
line=dict(width=0),
mode='lines',
fillcolor='rgba(47, 47, 162, 0.5)',
fill='tonexty',
showlegend=False
),
])
fig.update_layout(
yaxis_title='y',
# y轴范围=[-0.2,1],
title='Mixture Density Network Prediction',
hovermode="x",
yaxis_range=[df['target'].min()*0.85, df['target'].max()*1.15]
)
fig.write_image("imgs/prob_reg_mixing1_3.png")
fig = go.Figure([
go.Scatter(
name='Ground Truth',
x=df['col1'],
y=df['target'],
mode='markers',
line=dict(color='rgba(153, 115, 142, 0.2)'),
),
go.Scatter(
name='Component 2',
x=pred_df['col1'],
y=pred_df['mu_1'],
mode='lines',
line=dict(color='rgba(246, 76, 114, 1)'),
),
go.Scatter(
name='Mixing Coefficient 2',
x=pred_df['col1'],
y=pred_df['pi_1'],
mode='lines',
line=dict(color='rgba(255, 216, 117, 1)'),
),
go.Scatter(
name='Upper Bound 2',
x=pred_df['col1'],
y=pred_df['mu_1']+pred_df['sigma_1'],
mode='lines',
marker=dict(color='rgba(250, 152, 174, 0.5)'),
# 线宽=字典（宽度=0）
showlegend=False
),
go.Scatter(
name='Lower Bound 2',
x=pred_df['col1'],
y=pred_df['mu_1']-pred_df['sigma_1'],
marker=dict(color="#444"),
line=dict(width=0),
mode='lines',
fillcolor='rgba(250, 152, 174, 0.5)',
fill='tonexty',
showlegend=False
),
])
fig.update_layout(
yaxis_title='y',
# y轴范围=[-0.2, 1],
title='Mixture Density Network Prediction',
hovermode="x",
yaxis_range=[df['target'].min()*0.85, df['target'].max()*1.15]
)
fig.write_image("imgs/prob_reg_mixing2_3.png")
波士顿住房数据集¶
from sklearn.datasets import fetch_california_housing
target_col = "target"
data = fetch_california_housing(return_X_y=False)
X = pd.DataFrame(data['data'], columns=data['feature_names'])
cont_cols = X.columns.tolist()
cat_cols = []
y = data['target']
X[target_col] = y
df_train, df_test = train_test_split(X, test_size=0.2, random_state=42)
df_train, df_valid = train_test_split(df_train, test_size=0.2, random_state=42)
绘图¶
训练MDN¶
定义配置¶
让我们使用该软件包中的一个方便的工具函数来找出可能的高斯成分的中心。它内部运行K均值算法，并返回聚类中心，我们可以将其设置为偏置初始化。
epochs = 1000
batch_size = 2048
steps_per_epoch = int((len(df_train)//batch_size)*0.9)
data_config = DataConfig(
target=['target'],
continuous_cols=cont_cols,
categorical_cols=cat_cols,
# 连续特征变换="quantile_uniform"
)
trainer_config = TrainerConfig(
auto_lr_find=True, # 运行LRFinder以自动推导学习率
batch_size=batch_size,
max_epochs=epochs,
early_stopping="valid_loss",
early_stopping_patience=5,
checkpoints="valid_loss",
load_best=True
)
optimizer_config = OptimizerConfig(lr_scheduler="ReduceLROnPlateau", lr_scheduler_params={"patience":3})
mdn_head_config = MixtureDensityHeadConfig(
num_gaussian=4,
weight_regularization=2,
mu_bias_init=mu_init
).__dict__
#lambda_pi=10
#lambda_sigma=1
backbone_config_class = "CategoryEmbeddingModelConfig"
backbone_config = dict(
task="backbone",
layers="200-100", # 每层的节点数量
activation="ReLU", # 各层之间的激活
head=None,
)
model_config = MDNConfig(
task="regression",
backbone_config_class=backbone_config_class,
backbone_config_params=backbone_config,
head_config=mdn_head_config,
learning_rate=1e-3,
)
tabular_model = TabularModel(
data_config=data_config,
model_config=model_config,
optimizer_config=optimizer_config,
trainer_config=trainer_config
)
训练模型¶
预测与可视化¶
import scipy.stats as ss
def plot_normal(x_range, mu=0, sigma=1, cdf=False, **kwargs):
'''
Plots the normal distribution function for a given x range
If mu and sigma are not provided, standard normal is plotted
If cdf=True cumulative distribution is plotted
Passes any keyword arguments to matplotlib plot function
'''
x = x_range
if cdf:
y = ss.norm.cdf(x, mu, sigma)
else:
y = ss.norm.pdf(x, mu, sigma)
return x,y
def get_pdf(idx):
row = pred_df.iloc[idx]
pi = torch.from_numpy(row[['pi_0','pi_1','pi_2','pi_3']].values).unsqueeze(0)
mu = torch.from_numpy(row[['mu_0','mu_1','mu_2','mu_3']].values).unsqueeze(0)
sigma = torch.from_numpy(row[['sigma_0','sigma_1','sigma_2','sigma_3']].values).unsqueeze(0)
softmax_pi = nn.functional.gumbel_softmax(pi, tau=1, dim=-1)
categorical = Categorical(softmax_pi)
pis = categorical.sample().unsqueeze(1)
sigma = sigma.gather(1, pis).item()
mu = mu.gather(1, pis).item()
x = np.linspace(row['target_prediction'].item()*0.1, row['target_prediction'].item()*1.9, 5000)
return plot_normal(x, mu=mu, sigma=sigma)
traces = []
for idx in idxs:
x,y = get_pdf(idx)
trace = go.Scatter(
name=f'House_{idx}',
x=x,
y=y,
mode='lines',
# line=dict(color='rgba(246, 76, 114, 1)'),
)
traces.append(trace)
fig = go.Figure(traces)
fig.update_layout(
yaxis_title='P(MEDV)',
xaxis_title='MEDV',
# y轴范围=[-0.2,1],
title='PDFs of different Houses',
hovermode="x"
)
fig.write_image("imgs/prob_reg_pdfs_4.png")
