符号回归与分类¶

符号回归与分类旨在优化可解释的代数方程。TPOT 允许您将此方法与经典的机器学习操作相结合。

我们可以使用 TreePipeline 或 GraphSearchPipeline 构建符号方程的搜索空间，因为它们都没有固定的管道结构，而是优化其自身的序列和结构。

该策略是将叶节点设置为选择单个特征（使用 FSSNode），所有内部节点为算术运算符，根节点为分类器或回归器。

注意：这仍处于实验阶段。优化过程有很多优化机会。未来，符号回归/分类可能拥有自己的专用搜索空间类。

In [1]

import tpot
from tpot.search_spaces.pipelines import GraphSearchPipeline
from tpot.search_spaces.nodes import FSSNode
from tpot.config import get_search_space
import sklearn.datasets
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
import numpy as np

import tpot from tpot.search_spaces.pipelines import GraphSearchPipeline from tpot.search_spaces.nodes import FSSNode from tpot.config import get_search_space import sklearn.datasets from sklearn.linear_model import LogisticRegression from sklearn.model_selection import train_test_split import numpy as np

/opt/anaconda3/envs/tpotenv/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
  from .autonotebook import tqdm as notebook_tqdm

符号分类¶

In [2]

X, y = sklearn.datasets.make_classification(n_samples=1000, n_features=100, n_informative=6, n_redundant=0, n_repeated=0, n_classes=2, n_clusters_per_class=2, weights=None, flip_y=0.01, class_sep=1.0, hypercube=True, shift=0.0, scale=1.0, shuffle=True, random_state=None)
X_train, X_test, y_train, y_test = sklearn.model_selection.train_test_split(X, y, train_size=0.75, test_size=0.25)

X, y = sklearn.datasets.make_classification(n_samples=1000, n_features=100, n_informative=6, n_redundant=0, n_repeated=0, n_classes=2, n_clusters_per_class=2, weights=None, flip_y=0.01, class_sep=1.0, hypercube=True, shift=0.0, scale=1.0, shuffle=True, random_state=None) X_train, X_test, y_train, y_test = sklearn.model_selection.train_test_split(X, y, train_size=0.75, test_size=0.25)

In [3]

symbolic_classification_search_space = GraphSearchPipeline(
    root_search_space= get_search_space("LogisticRegression"),
    leaf_search_space = FSSNode(subsets=X_train.shape[1]), 
    inner_search_space = get_search_space(["arithmatic"]),
    max_size = 20,
)

#example pipelines randomly sampled
ind = symbolic_classification_search_space.generate(rng=5)
for i in range(3):
    ind.mutate(rng=1)
est_example = ind.export_pipeline()
est_example.plot()

symbolic_classification_search_space = GraphSearchPipeline( root_search_space= get_search_space("LogisticRegression"), leaf_search_space = FSSNode(subsets=X_train.shape[1]), inner_search_space = get_search_space(["arithmatic"]), max_size = 20, ) #随机采样的管道示例 ind = symbolic_classification_search_space.generate(rng=5) for i in range(3): ind.mutate(rng=1) est_example = ind.export_pipeline() est_example.plot()

In [4]

est = tpot.TPOTEstimator(  generations=20, 
                            max_time_mins=None,
                            scorers=['roc_auc_ovr'],
                            scorers_weights=[1],
                            other_objective_functions=[tpot.objectives.number_of_nodes_objective],
                            other_objective_functions_weights=[-1],
                            n_jobs=32,
                            classification=True,
                            search_space = symbolic_classification_search_space,
                            verbose=1,
                            )

scorer = sklearn.metrics.get_scorer('roc_auc_ovo')

est.fit(X_train, y_train)
print(scorer(est, X_test, y_test))
est.fitted_pipeline_.plot()

est = tpot.TPOTEstimator( generations=20, max_time_mins=None, scorers=['roc_auc_ovr'], scorers_weights=[1], other_objective_functions=[tpot.objectives.number_of_nodes_objective], other_objective_functions_weights=[-1], n_jobs=32, classification=True, search_space = symbolic_classification_search_space, verbose=1, ) scorer = sklearn.metrics.get_scorer('roc_auc_ovo') est.fit(X_train, y_train) print(scorer(est, X_test, y_test)) est.fitted_pipeline_.plot()

Generation: 100%|██████████| 20/20 [00:40<00:00,  2.01s/it]

0.71168

In [5]

import seaborn as sns
import matplotlib.pyplot as plt
df = est.evaluated_individuals
#replace nans in pareto front with 0
fig, ax = plt.subplots(figsize=(5,5))
sns.scatterplot(df[df['Pareto_Front']!=1], y='roc_auc_score', x='number_of_nodes_objective', label='other', ax=ax)
sns.scatterplot(df[df['Pareto_Front']==1], y='roc_auc_score', x='number_of_nodes_objective', label='Pareto Front', ax=ax)
ax.title.set_text('Performance of all pipelines')
#log scale y
plt.show()

#replace nans in pareto front with 0
fig, ax = plt.subplots(figsize=(10,5))
sns.scatterplot(df[df['Pareto_Front']==1], y='roc_auc_score', x='number_of_nodes_objective', label='Pareto Front', ax=ax)
ax.title.set_text('Performance of only the Pareto Front')
#log scale y
# ax.set_yscale('log')
plt.show()

import seaborn as sns import matplotlib.pyplot as plt df = est.evaluated_individuals #将帕累托前沿中的 NaN 替换为 0 fig, ax = plt.subplots(figsize=(5,5)) sns.scatterplot(df[df['Pareto_Front']!=1], y='roc_auc_score', x='number_of_nodes_objective', label='其他', ax=ax) sns.scatterplot(df[df['Pareto_Front']==1], y='roc_auc_score', x='number_of_nodes_objective', label='帕累托前沿', ax=ax) ax.title.set_text('所有管道的性能') #y轴对数刻度 plt.show() #将帕累托前沿中的 NaN 替换为 0 fig, ax = plt.subplots(figsize=(10,5)) sns.scatterplot(df[df['Pareto_Front']==1], y='roc_auc_score', x='number_of_nodes_objective', label='帕累托前沿', ax=ax) ax.title.set_text('仅帕累托前沿的性能') #y轴对数刻度 # ax.set_yscale('log') plt.show()

符号回归

In [6]

import tpot
import sklearn.datasets

scorer = sklearn.metrics.get_scorer('neg_mean_squared_error')
X, y = sklearn.datasets.load_diabetes(return_X_y=True)
X_train, X_test, y_train, y_test = sklearn.model_selection.train_test_split(X, y, train_size=0.75, test_size=0.25)

graph_search_space = tpot.search_spaces.pipelines.GraphSearchPipeline(
    root_search_space= tpot.config.get_search_space("SGDRegressor"),
    leaf_search_space = tpot.search_spaces.nodes.FSSNode(subsets=X_train.shape[1]), 
    inner_search_space = tpot.config.get_search_space(["arithmatic"]),
    max_size = 10,
)

est = tpot.TPOTEstimator(  generations=20, 
                            max_time_mins=None,
                            scorers=['neg_mean_squared_error'],
                            scorers_weights=[1],
                            other_objective_functions=[tpot.objectives.number_of_nodes_objective],
                            other_objective_functions_weights=[-1],
                            n_jobs=32,
                            classification=False,
                            search_space = graph_search_space ,
                            verbose=2,
                            )



est.fit(X_train, y_train)
print(scorer(est, X_test, y_test))
est.fitted_pipeline_.plot()

import tpot import sklearn.datasets scorer = sklearn.metrics.get_scorer('neg_mean_squared_error') X, y = sklearn.datasets.load_diabetes(return_X_y=True) X_train, X_test, y_train, y_test = sklearn.model_selection.train_test_split(X, y, train_size=0.75, test_size=0.25) graph_search_space = tpot.search_spaces.pipelines.GraphSearchPipeline( root_search_space= tpot.config.get_search_space("SGDRegressor"), leaf_search_space = tpot.search_spaces.nodes.FSSNode(subsets=X_train.shape[1]), inner_search_space = tpot.config.get_search_space(["arithmatic"]), max_size = 10, ) est = tpot.TPOTEstimator( generations=20, max_time_mins=None, scorers=['neg_mean_squared_error'], scorers_weights=[1], other_objective_functions=[tpot.objectives.number_of_nodes_objective], other_objective_functions_weights=[-1], n_jobs=32, classification=False, search_space = graph_search_space , verbose=2, ) est.fit(X_train, y_train) print(scorer(est, X_test, y_test)) est.fitted_pipeline_.plot()

Generation: 100%|██████████| 20/20 [00:30<00:00,  1.51s/it]

-3073.9914754941187

In [7]

import seaborn as sns
import matplotlib.pyplot as plt
df = est.evaluated_individuals
df['mean_squared_error'] = -df['mean_squared_error']
#replace nans in pareto front with 0
fig, ax = plt.subplots(figsize=(5,5))
sns.scatterplot(df[df['Pareto_Front']!=1], y='mean_squared_error', x='number_of_nodes_objective', label='other', ax=ax)
sns.scatterplot(df[df['Pareto_Front']==1], y='mean_squared_error', x='number_of_nodes_objective', label='Pareto Front', ax=ax)
ax.title.set_text('Performance of all pipelines')
#log scale y
ax.set_yscale('log')
plt.show()

#replace nans in pareto front with 0
fig, ax = plt.subplots(figsize=(10,5))
sns.scatterplot(df[df['Pareto_Front']==1], y='mean_squared_error', x='number_of_nodes_objective', label='Pareto Front', ax=ax)
ax.title.set_text('Performance of only the Pareto Front')
#log scale y
# ax.set_yscale('log')
plt.show()

import seaborn as sns import matplotlib.pyplot as plt df = est.evaluated_individuals df['mean_squared_error'] = -df['mean_squared_error'] #将帕累托前沿中的 NaN 替换为 0 fig, ax = plt.subplots(figsize=(5,5)) sns.scatterplot(df[df['Pareto_Front']!=1], y='mean_squared_error', x='number_of_nodes_objective', label='其他', ax=ax) sns.scatterplot(df[df['Pareto_Front']==1], y='mean_squared_error', x='number_of_nodes_objective', label='帕累托前沿', ax=ax) ax.title.set_text('所有管道的性能') #y轴对数刻度 ax.set_yscale('log') plt.show() #将帕累托前沿中的 NaN 替换为 0 fig, ax = plt.subplots(figsize=(10,5)) sns.scatterplot(df[df['Pareto_Front']==1], y='mean_squared_error', x='number_of_nodes_objective', label='帕累托前沿', ax=ax) ax.title.set_text('仅帕累托前沿的性能') #y轴对数刻度 # ax.set_yscale('log') plt.show()