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Pytorch Widedeep

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未验证 提交 8b4c3a8a 编写于 作者: P Pavol Mulinka 提交者： GitHub
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Merge pull request #71 from jrzaurin/pmulinka/auc 

Pmulinka/auc - a small fix related to torchmetrics
上级 88790c39 f17fe842
隐藏空白更改
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Showing with 900 addition and 19 deletion
VERSION
浏览文件 @ 8b4c3a8a
1.0.13
\ No newline at end of file
1.0.14
\ No newline at end of file
examples/15_AUC_multiclass.ipynb 0 → 100644
浏览文件 @ 8b4c3a8a
{
"cells": [
{
"cell_type": "markdown",
"id": "731975e2",
"metadata": {},
"source": [
"# AUC multiclass computation"
]
},
{
"cell_type": "markdown",
"id": "ee745c58",
"metadata": {},
"source": [
"## Initial imports"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "fdab94eb",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2021-12-11 21:51:29.255591: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory\n",
"2021-12-11 21:51:29.255638: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\n"
]
}
],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"from torch.optim import SGD, lr_scheduler\n",
"\n",
"from pytorch_widedeep import Trainer\n",
"from pytorch_widedeep.preprocessing import TabPreprocessor\n",
"from pytorch_widedeep.models import TabMlp, WideDeep\n",
"from torchmetrics import AUC, AUROC\n",
"from pytorch_widedeep.initializers import XavierNormal\n",
"from pytorch_widedeep.datasets import load_ecoli\n",
"from pytorch_widedeep.utils import LabelEncoder\n",
"\n",
"from sklearn.model_selection import train_test_split\n",
"\n",
"# increase displayed columns in jupyter notebook\n",
"pd.set_option(\"display.max_columns\", 200)\n",
"pd.set_option(\"display.max_rows\", 300)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "07c75f0c",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>SequenceName</th>\n",
" <th>mcg</th>\n",
" <th>gvh</th>\n",
" <th>lip</th>\n",
" <th>chg</th>\n",
" <th>aac</th>\n",
" <th>alm1</th>\n",
" <th>alm2</th>\n",
" <th>class</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>AAT_ECOLI</td>\n",
" <td>0.49</td>\n",
" <td>0.29</td>\n",
" <td>0.48</td>\n",
" <td>0.5</td>\n",
" <td>0.56</td>\n",
" <td>0.24</td>\n",
" <td>0.35</td>\n",
" <td>cp</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>ACEA_ECOLI</td>\n",
" <td>0.07</td>\n",
" <td>0.40</td>\n",
" <td>0.48</td>\n",
" <td>0.5</td>\n",
" <td>0.54</td>\n",
" <td>0.35</td>\n",
" <td>0.44</td>\n",
" <td>cp</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>ACEK_ECOLI</td>\n",
" <td>0.56</td>\n",
" <td>0.40</td>\n",
" <td>0.48</td>\n",
" <td>0.5</td>\n",
" <td>0.49</td>\n",
" <td>0.37</td>\n",
" <td>0.46</td>\n",
" <td>cp</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>ACKA_ECOLI</td>\n",
" <td>0.59</td>\n",
" <td>0.49</td>\n",
" <td>0.48</td>\n",
" <td>0.5</td>\n",
" <td>0.52</td>\n",
" <td>0.45</td>\n",
" <td>0.36</td>\n",
" <td>cp</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>ADI_ECOLI</td>\n",
" <td>0.23</td>\n",
" <td>0.32</td>\n",
" <td>0.48</td>\n",
" <td>0.5</td>\n",
" <td>0.55</td>\n",
" <td>0.25</td>\n",
" <td>0.35</td>\n",
" <td>cp</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" SequenceName mcg gvh lip chg aac alm1 alm2 class\n",
"0 AAT_ECOLI 0.49 0.29 0.48 0.5 0.56 0.24 0.35 cp\n",
"1 ACEA_ECOLI 0.07 0.40 0.48 0.5 0.54 0.35 0.44 cp\n",
"2 ACEK_ECOLI 0.56 0.40 0.48 0.5 0.49 0.37 0.46 cp\n",
"3 ACKA_ECOLI 0.59 0.49 0.48 0.5 0.52 0.45 0.36 cp\n",
"4 ADI_ECOLI 0.23 0.32 0.48 0.5 0.55 0.25 0.35 cp"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = load_ecoli(as_frame=True)\n",
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "1e3f8efc",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"cp 143\n",
"im 77\n",
"pp 52\n",
"imU 35\n",
"om 20\n",
"omL 5\n",
"imS 2\n",
"imL 2\n",
"Name: class, dtype: int64"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# imbalance of the classes\n",
"df[\"class\"].value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "e4db0d6d",
"metadata": {},
"outputs": [],
"source": [
"df = df.loc[~df[\"class\"].isin([\"omL\", \"imS\", \"imL\"])]\n",
"df.reset_index(inplace=True, drop=True)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "005531a3",
"metadata": {},
"outputs": [],
"source": [
"encoder = LabelEncoder([\"class\"])\n",
"df_enc = encoder.fit_transform(df)\n",
"df_enc[\"class\"] = df_enc[\"class\"]-1"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "214b3071",
"metadata": {},
"outputs": [],
"source": [
"# drop columns we won't need in this example\n",
"df_enc = df_enc.drop(columns=[\"SequenceName\"])"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "168c81f1",
"metadata": {},
"outputs": [],
"source": [
"df_train, df_valid = train_test_split(df_enc, test_size=0.2, stratify=df_enc[\"class\"], random_state=1)\n",
"df_valid, df_test = train_test_split(df_valid, test_size=0.5, stratify=df_valid[\"class\"], random_state=1)"
]
},
{
"cell_type": "markdown",
"id": "87e7b8f0",
"metadata": {},
"source": [
"## Preparing the data"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "3a7b246b",
"metadata": {},
"outputs": [],
"source": [
"continuous_cols = df_enc.drop(columns=[\"class\"]).columns.values.tolist()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "7a2dac24",
"metadata": {},
"outputs": [],
"source": [
"# deeptabular\n",
"tab_preprocessor = TabPreprocessor(continuous_cols=continuous_cols, scale=True)\n",
"X_tab_train = tab_preprocessor.fit_transform(df_train)\n",
"X_tab_valid = tab_preprocessor.transform(df_valid)\n",
"X_tab_test = tab_preprocessor.transform(df_test)\n",
"\n",
"# target\n",
"y_train = df_train[\"class\"].values\n",
"y_valid = df_valid[\"class\"].values\n",
"y_test = df_test[\"class\"].values\n",
"\n",
"X_train = {\"X_tab\": X_tab_train, \"target\": y_train}\n",
"X_val = {\"X_tab\": X_tab_valid, \"target\": y_valid}"
]
},
{
"cell_type": "markdown",
"id": "7b9f63e2",
"metadata": {},
"source": [
"## Define the model"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "511198d4",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"WideDeep(\n",
" (deeptabular): Sequential(\n",
" (0): TabMlp(\n",
" (cat_embed_and_cont): CatEmbeddingsAndCont(\n",
" (cont_norm): BatchNorm1d(7, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" (tab_mlp): MLP(\n",
" (mlp): Sequential(\n",
" (dense_layer_0): Sequential(\n",
" (0): Dropout(p=0.1, inplace=False)\n",
" (1): Linear(in_features=7, out_features=200, bias=True)\n",
" (2): ReLU(inplace=True)\n",
" )\n",
" (dense_layer_1): Sequential(\n",
" (0): Dropout(p=0.1, inplace=False)\n",
" (1): Linear(in_features=200, out_features=100, bias=True)\n",
" (2): ReLU(inplace=True)\n",
" )\n",
" )\n",
" )\n",
" )\n",
" (1): Linear(in_features=100, out_features=5, bias=True)\n",
" )\n",
")"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"deeptabular = TabMlp(\n",
" column_idx=tab_preprocessor.column_idx,\n",
" continuous_cols=tab_preprocessor.continuous_cols,\n",
")\n",
"model = WideDeep(deeptabular=deeptabular, pred_dim=df_enc[\"class\"].nunique())\n",
"model"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "a5359b0f",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/palo/miniconda3/lib/python3.8/site-packages/torchmetrics/utilities/prints.py:36: UserWarning: Metric `AUROC` will save all targets and predictions in buffer. For large datasets this may lead to large memory footprint.\n",
" warnings.warn(*args, **kwargs)\n"
]
}
],
"source": [
"auroc = AUROC(num_classes=df_enc[\"class\"].nunique())"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "34a18ac0",
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"epoch 1: 100%|██████████| 6/6 [00:00<00:00, 84.27it/s, loss=0.111, metrics={'AUROC': 0.285}]\n",
"valid: 100%|██████████| 1/1 [00:00<00:00, 5.57it/s, loss=0.106, metrics={'AUROC': 0.3309}]\n",
"epoch 2: 100%|██████████| 6/6 [00:00<00:00, 111.92it/s, loss=0.106, metrics={'AUROC': 0.3124}]\n",
"valid: 100%|██████████| 1/1 [00:00<00:00, 4.99it/s, loss=0.102, metrics={'AUROC': 0.375}]\n",
"epoch 3: 100%|██████████| 6/6 [00:00<00:00, 109.51it/s, loss=0.102, metrics={'AUROC': 0.3459}]\n",
"valid: 100%|██████████| 1/1 [00:00<00:00, 6.70it/s, loss=0.0967, metrics={'AUROC': 0.4444}]\n",
"epoch 4: 100%|██████████| 6/6 [00:00<00:00, 106.40it/s, loss=0.0984, metrics={'AUROC': 0.3717}]\n",
"valid: 100%|██████████| 1/1 [00:00<00:00, 5.93it/s, loss=0.0963, metrics={'AUROC': 0.4516}]\n",
"epoch 5: 100%|██████████| 6/6 [00:00<00:00, 93.06it/s, loss=0.0975, metrics={'AUROC': 0.3877}]\n",
"valid: 100%|██████████| 1/1 [00:00<00:00, 5.98it/s, loss=0.0961, metrics={'AUROC': 0.4404}]\n"
]
}
],
"source": [
"# Optimizers\n",
"deep_opt = SGD(model.deeptabular.parameters(), lr=0.1)\n",
"# LR Scheduler\n",
"deep_sch = lr_scheduler.StepLR(deep_opt, step_size=3)\n",
"# Hyperparameters\n",
"trainer = Trainer(\n",
" model,\n",
" objective=\"multiclass_focal_loss\",\n",
" lr_schedulers={\"deeptabular\": deep_sch},\n",
" initializers={\"deeptabular\": XavierNormal},\n",
" optimizers={\"deeptabular\": deep_opt},\n",
" metrics=[auroc],\n",
")\n",
"\n",
"trainer.fit(X_train=X_train, X_val=X_val, n_epochs=5, batch_size=50)"
]
}
],
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pytorch_widedeep/datasets/__init__.py
浏览文件 @ 8b4c3a8a
from ._base import load_adult, load_bio_kdd04
from ._base import load_adult, load_bio_kdd04, load_ecoli
__all__ = ["load_bio_kdd04", "load_adult"]
__all__ = ["load_bio_kdd04", "load_adult", "load_ecoli"]
pytorch_widedeep/datasets/_base.py
浏览文件 @ 8b4c3a8a
......@@ -5,7 +5,7 @@ import pandas as pd
def load_bio_kdd04(as_frame: bool = False):
"""Load and return the higly imbalanced Protein Homology
Dataset from [KDD cup 2004](https://www.kdd.org/kdd-cup/view/kdd-cup-2004/Data.
Dataset from [KDD cup 2004](https://www.kdd.org/kdd-cup/view/kdd-cup-2004/Data).
This datasets include only bio_train.dat part of the dataset
......@@ -46,3 +46,88 @@ def load_adult(as_frame: bool = False):
return df
else:
return df.to_numpy()
def load_ecoli(as_frame: bool = False):
"""Load and return the higly imbalanced multiclass classification e.coli dataset
Dataset from [UCI Machine learning Repository](https://archive.ics.uci.edu/ml/datasets/ecoli).
1. Title: Protein Localization Sites
2. Creator and Maintainer:
Kenta Nakai
Institue of Molecular and Cellular Biology
Osaka, University
1-3 Yamada-oka, Suita 565 Japan
nakai@imcb.osaka-u.ac.jp
http://www.imcb.osaka-u.ac.jp/nakai/psort.html
Donor: Paul Horton (paulh@cs.berkeley.edu)
Date: September, 1996
See also: yeast database
3. Past Usage.
Reference: "A Probablistic Classification System for Predicting the Cellular
Localization Sites of Proteins", Paul Horton & Kenta Nakai,
Intelligent Systems in Molecular Biology, 109-115.
St. Louis, USA 1996.
Results: 81% for E.coli with an ad hoc structured
probability model. Also similar accuracy for Binary Decision Tree and
Bayesian Classifier methods applied by the same authors in
unpublished results.
Predicted Attribute: Localization site of protein. ( non-numeric ).
4. The references below describe a predecessor to this dataset and its
development. They also give results (not cross-validated) for classification
by a rule-based expert system with that version of the dataset.
Reference: "Expert Sytem for Predicting Protein Localization Sites in
Gram-Negative Bacteria", Kenta Nakai & Minoru Kanehisa,
PROTEINS: Structure, Function, and Genetics 11:95-110, 1991.
Reference: "A Knowledge Base for Predicting Protein Localization Sites in
Eukaryotic Cells", Kenta Nakai & Minoru Kanehisa,
Genomics 14:897-911, 1992.
5. Number of Instances: 336 for the E.coli dataset and
6. Number of Attributes.
for E.coli dataset: 8 ( 7 predictive, 1 name )
7. Attribute Information.
1. Sequence Name: Accession number for the SWISS-PROT database
2. mcg: McGeoch's method for signal sequence recognition.
3. gvh: von Heijne's method for signal sequence recognition.
4. lip: von Heijne's Signal Peptidase II consensus sequence score.
Binary attribute.
5. chg: Presence of charge on N-terminus of predicted lipoproteins.
Binary attribute.
6. aac: score of discriminant analysis of the amino acid content of
outer membrane and periplasmic proteins.
7. alm1: score of the ALOM membrane spanning region prediction program.
8. alm2: score of ALOM program after excluding putative cleavable signal
regions from the sequence.
8. Missing Attribute Values: None.
9. Class Distribution. The class is the localization site. Please see Nakai & Kanehisa referenced above for more details.
cp (cytoplasm) 143
im (inner membrane without signal sequence) 77
pp (perisplasm) 52
imU (inner membrane, uncleavable signal sequence) 35
om (outer membrane) 20
omL (outer membrane lipoprotein) 5
imL (inner membrane lipoprotein) 2
imS (inner membrane, cleavable signal sequence) 2
"""
with resources.path("pytorch_widedeep.datasets.data", "ecoli.csv") as fpath:
df = pd.read_csv(fpath, sep=",")
if as_frame:
return df
else:
return df.to_numpy()
pytorch_widedeep/datasets/data/ecoli.csv 0 → 100644
浏览文件 @ 8b4c3a8a
SequenceName,mcg,gvh,lip,chg,aac,alm1,alm2,class
AAT_ECOLI,0.49,0.29,0.48,0.50,0.56,0.24,0.35,cp
ACEA_ECOLI,0.07,0.40,0.48,0.50,0.54,0.35,0.44,cp
ACEK_ECOLI,0.56,0.40,0.48,0.50,0.49,0.37,0.46,cp
ACKA_ECOLI,0.59,0.49,0.48,0.50,0.52,0.45,0.36,cp
ADI_ECOLI,0.23,0.32,0.48,0.50,0.55,0.25,0.35,cp
ALKH_ECOLI,0.67,0.39,0.48,0.50,0.36,0.38,0.46,cp
AMPD_ECOLI,0.29,0.28,0.48,0.50,0.44,0.23,0.34,cp
AMY2_ECOLI,0.21,0.34,0.48,0.50,0.51,0.28,0.39,cp
APT_ECOLI,0.20,0.44,0.48,0.50,0.46,0.51,0.57,cp
ARAC_ECOLI,0.42,0.40,0.48,0.50,0.56,0.18,0.30,cp
ASG1_ECOLI,0.42,0.24,0.48,0.50,0.57,0.27,0.37,cp
BTUR_ECOLI,0.25,0.48,0.48,0.50,0.44,0.17,0.29,cp
CAFA_ECOLI,0.39,0.32,0.48,0.50,0.46,0.24,0.35,cp
CAIB_ECOLI,0.51,0.50,0.48,0.50,0.46,0.32,0.35,cp
CFA_ECOLI,0.22,0.43,0.48,0.50,0.48,0.16,0.28,cp
CHEA_ECOLI,0.25,0.40,0.48,0.50,0.46,0.44,0.52,cp
CHEB_ECOLI,0.34,0.45,0.48,0.50,0.38,0.24,0.35,cp
CHEW_ECOLI,0.44,0.27,0.48,0.50,0.55,0.52,0.58,cp
CHEY_ECOLI,0.23,0.40,0.48,0.50,0.39,0.28,0.38,cp
CHEZ_ECOLI,0.41,0.57,0.48,0.50,0.39,0.21,0.32,cp
CRL_ECOLI,0.40,0.45,0.48,0.50,0.38,0.22,0.00,cp
CSPA_ECOLI,0.31,0.23,0.48,0.50,0.73,0.05,0.14,cp
CYNR_ECOLI,0.51,0.54,0.48,0.50,0.41,0.34,0.43,cp
CYPB_ECOLI,0.30,0.16,0.48,0.50,0.56,0.11,0.23,cp
CYPC_ECOLI,0.36,0.39,0.48,0.50,0.48,0.22,0.23,cp
CYSB_ECOLI,0.29,0.37,0.48,0.50,0.48,0.44,0.52,cp
CYSE_ECOLI,0.25,0.40,0.48,0.50,0.47,0.33,0.42,cp
DAPD_ECOLI,0.21,0.51,0.48,0.50,0.50,0.32,0.41,cp
DCP_ECOLI,0.43,0.37,0.48,0.50,0.53,0.35,0.44,cp
DDLA_ECOLI,0.43,0.39,0.48,0.50,0.47,0.31,0.41,cp
DDLB_ECOLI,0.53,0.38,0.48,0.50,0.44,0.26,0.36,cp
DEOC_ECOLI,0.34,0.33,0.48,0.50,0.38,0.35,0.44,cp
DLDH_ECOLI,0.56,0.51,0.48,0.50,0.34,0.37,0.46,cp
EFG_ECOLI,0.40,0.29,0.48,0.50,0.42,0.35,0.44,cp
EFTS_ECOLI,0.24,0.35,0.48,0.50,0.31,0.19,0.31,cp
EFTU_ECOLI,0.36,0.54,0.48,0.50,0.41,0.38,0.46,cp
ENO_ECOLI,0.29,0.52,0.48,0.50,0.42,0.29,0.39,cp
FABB_ECOLI,0.65,0.47,0.48,0.50,0.59,0.30,0.40,cp
FES_ECOLI,0.32,0.42,0.48,0.50,0.35,0.28,0.38,cp
G3P1_ECOLI,0.38,0.46,0.48,0.50,0.48,0.22,0.29,cp
G3P2_ECOLI,0.33,0.45,0.48,0.50,0.52,0.32,0.41,cp
G6PI_ECOLI,0.30,0.37,0.48,0.50,0.59,0.41,0.49,cp
GCVA_ECOLI,0.40,0.50,0.48,0.50,0.45,0.39,0.47,cp
GLNA_ECOLI,0.28,0.38,0.48,0.50,0.50,0.33,0.42,cp
GLPD_ECOLI,0.61,0.45,0.48,0.50,0.48,0.35,0.41,cp
GLYA_ECOLI,0.17,0.38,0.48,0.50,0.45,0.42,0.50,cp
GSHR_ECOLI,0.44,0.35,0.48,0.50,0.55,0.55,0.61,cp
GT_ECOLI,0.43,0.40,0.48,0.50,0.39,0.28,0.39,cp
HEM6_ECOLI,0.42,0.35,0.48,0.50,0.58,0.15,0.27,cp
HEMN_ECOLI,0.23,0.33,0.48,0.50,0.43,0.33,0.43,cp
HPRT_ECOLI,0.37,0.52,0.48,0.50,0.42,0.42,0.36,cp
IF1_ECOLI,0.29,0.30,0.48,0.50,0.45,0.03,0.17,cp
IF2_ECOLI,0.22,0.36,0.48,0.50,0.35,0.39,0.47,cp
ILVY_ECOLI,0.23,0.58,0.48,0.50,0.37,0.53,0.59,cp
IPYR_ECOLI,0.47,0.47,0.48,0.50,0.22,0.16,0.26,cp
KAD_ECOLI,0.54,0.47,0.48,0.50,0.28,0.33,0.42,cp
KDSA_ECOLI,0.51,0.37,0.48,0.50,0.35,0.36,0.45,cp
LEU3_ECOLI,0.40,0.35,0.48,0.50,0.45,0.33,0.42,cp
LON_ECOLI,0.44,0.34,0.48,0.50,0.30,0.33,0.43,cp
LPLA_ECOLI,0.42,0.38,0.48,0.50,0.54,0.34,0.43,cp
LYSR_ECOLI,0.44,0.56,0.48,0.50,0.50,0.46,0.54,cp
MALQ_ECOLI,0.52,0.36,0.48,0.50,0.41,0.28,0.38,cp
MALZ_ECOLI,0.36,0.41,0.48,0.50,0.48,0.47,0.54,cp
MASY_ECOLI,0.18,0.30,0.48,0.50,0.46,0.24,0.35,cp
METB_ECOLI,0.47,0.29,0.48,0.50,0.51,0.33,0.43,cp
METC_ECOLI,0.24,0.43,0.48,0.50,0.54,0.52,0.59,cp
METK_ECOLI,0.25,0.37,0.48,0.50,0.41,0.33,0.42,cp
METR_ECOLI,0.52,0.57,0.48,0.50,0.42,0.47,0.54,cp
METX_ECOLI,0.25,0.37,0.48,0.50,0.43,0.26,0.36,cp
MURF_ECOLI,0.35,0.48,0.48,0.50,0.56,0.40,0.48,cp
NADA_ECOLI,0.26,0.26,0.48,0.50,0.34,0.25,0.35,cp
NFRC_ECOLI,0.44,0.51,0.48,0.50,0.47,0.26,0.36,cp
NHAR_ECOLI,0.37,0.50,0.48,0.50,0.42,0.36,0.45,cp
NIRD_ECOLI,0.44,0.42,0.48,0.50,0.42,0.25,0.20,cp
OMPR_ECOLI,0.24,0.43,0.48,0.50,0.37,0.28,0.38,cp
OTC1_ECOLI,0.42,0.30,0.48,0.50,0.48,0.26,0.36,cp
OTC2_ECOLI,0.48,0.42,0.48,0.50,0.45,0.25,0.35,cp
PEPE_ECOLI,0.41,0.48,0.48,0.50,0.51,0.44,0.51,cp
PFLA_ECOLI,0.44,0.28,0.48,0.50,0.43,0.27,0.37,cp
PFLB_ECOLI,0.29,0.41,0.48,0.50,0.48,0.38,0.46,cp
PGK_ECOLI,0.34,0.28,0.48,0.50,0.41,0.35,0.44,cp
PHOB_ECOLI,0.41,0.43,0.48,0.50,0.45,0.31,0.41,cp
PHOH_ECOLI,0.29,0.47,0.48,0.50,0.41,0.23,0.34,cp
PMBA_ECOLI,0.34,0.55,0.48,0.50,0.58,0.31,0.41,cp
PNP_ECOLI,0.36,0.56,0.48,0.50,0.43,0.45,0.53,cp
PROB_ECOLI,0.40,0.46,0.48,0.50,0.52,0.49,0.56,cp
PT1A_ECOLI,0.50,0.49,0.48,0.50,0.49,0.46,0.53,cp
PT1_ECOLI,0.52,0.44,0.48,0.50,0.37,0.36,0.42,cp
PTCA_ECOLI,0.50,0.51,0.48,0.50,0.27,0.23,0.34,cp
PTCB_ECOLI,0.53,0.42,0.48,0.50,0.16,0.29,0.39,cp
PTFA_ECOLI,0.34,0.46,0.48,0.50,0.52,0.35,0.44,cp
PTGA_ECOLI,0.40,0.42,0.48,0.50,0.37,0.27,0.27,cp
PTHA_ECOLI,0.41,0.43,0.48,0.50,0.50,0.24,0.25,cp
PTHP_ECOLI,0.30,0.45,0.48,0.50,0.36,0.21,0.32,cp
PTKA_ECOLI,0.31,0.47,0.48,0.50,0.29,0.28,0.39,cp
PTKB_ECOLI,0.64,0.76,0.48,0.50,0.45,0.35,0.38,cp
PTNA_ECOLI,0.35,0.37,0.48,0.50,0.30,0.34,0.43,cp
PTWB_ECOLI,0.57,0.54,0.48,0.50,0.37,0.28,0.33,cp
PTWX_ECOLI,0.65,0.55,0.48,0.50,0.34,0.37,0.28,cp
RHAR_ECOLI,0.51,0.46,0.48,0.50,0.58,0.31,0.41,cp
RHAS_ECOLI,0.38,0.40,0.48,0.50,0.63,0.25,0.35,cp
RIMI_ECOLI,0.24,0.57,0.48,0.50,0.63,0.34,0.43,cp
RIMJ_ECOLI,0.38,0.26,0.48,0.50,0.54,0.16,0.28,cp
RIML_ECOLI,0.33,0.47,0.48,0.50,0.53,0.18,0.29,cp
RNB_ECOLI,0.24,0.34,0.48,0.50,0.38,0.30,0.40,cp
RNC_ECOLI,0.26,0.50,0.48,0.50,0.44,0.32,0.41,cp
RND_ECOLI,0.44,0.49,0.48,0.50,0.39,0.38,0.40,cp
RNE_ECOLI,0.43,0.32,0.48,0.50,0.33,0.45,0.52,cp
SERC_ECOLI,0.49,0.43,0.48,0.50,0.49,0.30,0.40,cp
SLYD_ECOLI,0.47,0.28,0.48,0.50,0.56,0.20,0.25,cp
SOXS_ECOLI,0.32,0.33,0.48,0.50,0.60,0.06,0.20,cp
SYA_ECOLI,0.34,0.35,0.48,0.50,0.51,0.49,0.56,cp
SYC_ECOLI,0.35,0.34,0.48,0.50,0.46,0.30,0.27,cp
SYD_ECOLI,0.38,0.30,0.48,0.50,0.43,0.29,0.39,cp
SYE_ECOLI,0.38,0.44,0.48,0.50,0.43,0.20,0.31,cp
SYFA_ECOLI,0.41,0.51,0.48,0.50,0.58,0.20,0.31,cp
SYFB_ECOLI,0.34,0.42,0.48,0.50,0.41,0.34,0.43,cp
SYGA_ECOLI,0.51,0.49,0.48,0.50,0.53,0.14,0.26,cp
SYGB_ECOLI,0.25,0.51,0.48,0.50,0.37,0.42,0.50,cp
SYH_ECOLI,0.29,0.28,0.48,0.50,0.50,0.42,0.50,cp
SYI_ECOLI,0.25,0.26,0.48,0.50,0.39,0.32,0.42,cp
SYK1_ECOLI,0.24,0.41,0.48,0.50,0.49,0.23,0.34,cp
SYK2_ECOLI,0.17,0.39,0.48,0.50,0.53,0.30,0.39,cp
SYL_ECOLI,0.04,0.31,0.48,0.50,0.41,0.29,0.39,cp
SYM_ECOLI,0.61,0.36,0.48,0.50,0.49,0.35,0.44,cp
SYP_ECOLI,0.34,0.51,0.48,0.50,0.44,0.37,0.46,cp
SYQ_ECOLI,0.28,0.33,0.48,0.50,0.45,0.22,0.33,cp
SYR_ECOLI,0.40,0.46,0.48,0.50,0.42,0.35,0.44,cp
SYS_ECOLI,0.23,0.34,0.48,0.50,0.43,0.26,0.37,cp
SYT_ECOLI,0.37,0.44,0.48,0.50,0.42,0.39,0.47,cp
SYV_ECOLI,0.00,0.38,0.48,0.50,0.42,0.48,0.55,cp
SYW_ECOLI,0.39,0.31,0.48,0.50,0.38,0.34,0.43,cp
SYY_ECOLI,0.30,0.44,0.48,0.50,0.49,0.22,0.33,cp
THGA_ECOLI,0.27,0.30,0.48,0.50,0.71,0.28,0.39,cp
THIK_ECOLI,0.17,0.52,0.48,0.50,0.49,0.37,0.46,cp
TYRB_ECOLI,0.36,0.42,0.48,0.50,0.53,0.32,0.41,cp
UBIC_ECOLI,0.30,0.37,0.48,0.50,0.43,0.18,0.30,cp
UGPQ_ECOLI,0.26,0.40,0.48,0.50,0.36,0.26,0.37,cp
USPA_ECOLI,0.40,0.41,0.48,0.50,0.55,0.22,0.33,cp
UVRB_ECOLI,0.22,0.34,0.48,0.50,0.42,0.29,0.39,cp
UVRC_ECOLI,0.44,0.35,0.48,0.50,0.44,0.52,0.59,cp
XGPT_ECOLI,0.27,0.42,0.48,0.50,0.37,0.38,0.43,cp
XYLA_ECOLI,0.16,0.43,0.48,0.50,0.54,0.27,0.37,cp
EMRA_ECOLI,0.06,0.61,0.48,0.50,0.49,0.92,0.37,im
AAS_ECOLI,0.44,0.52,0.48,0.50,0.43,0.47,0.54,im
AMPE_ECOLI,0.63,0.47,0.48,0.50,0.51,0.82,0.84,im
ARAE_ECOLI,0.23,0.48,0.48,0.50,0.59,0.88,0.89,im
ARAH_ECOLI,0.34,0.49,0.48,0.50,0.58,0.85,0.80,im
AROP_ECOLI,0.43,0.40,0.48,0.50,0.58,0.75,0.78,im
ATKB_ECOLI,0.46,0.61,0.48,0.50,0.48,0.86,0.87,im
ATP6_ECOLI,0.27,0.35,0.48,0.50,0.51,0.77,0.79,im
BETT_ECOLI,0.52,0.39,0.48,0.50,0.65,0.71,0.73,im
CODB_ECOLI,0.29,0.47,0.48,0.50,0.71,0.65,0.69,im
CYDA_ECOLI,0.55,0.47,0.48,0.50,0.57,0.78,0.80,im
CYOC_ECOLI,0.12,0.67,0.48,0.50,0.74,0.58,0.63,im
CYOD_ECOLI,0.40,0.50,0.48,0.50,0.65,0.82,0.84,im
DCTA_ECOLI,0.73,0.36,0.48,0.50,0.53,0.91,0.92,im
DHG_ECOLI,0.84,0.44,0.48,0.50,0.48,0.71,0.74,im
DHSC_ECOLI,0.48,0.45,0.48,0.50,0.60,0.78,0.80,im
DHSD_ECOLI,0.54,0.49,0.48,0.50,0.40,0.87,0.88,im
DPPC_ECOLI,0.48,0.41,0.48,0.50,0.51,0.90,0.88,im
DSBB_ECOLI,0.50,0.66,0.48,0.50,0.31,0.92,0.92,im
ENVZ_ECOLI,0.72,0.46,0.48,0.50,0.51,0.66,0.70,im
EXBB_ECOLI,0.47,0.55,0.48,0.50,0.58,0.71,0.75,im
FRDC_ECOLI,0.33,0.56,0.48,0.50,0.33,0.78,0.80,im
FRDD_ECOLI,0.64,0.58,0.48,0.50,0.48,0.78,0.73,im
FTSW_ECOLI,0.54,0.57,0.48,0.50,0.56,0.81,0.83,im
GABP_ECOLI,0.47,0.59,0.48,0.50,0.52,0.76,0.79,im
GALP_ECOLI,0.63,0.50,0.48,0.50,0.59,0.85,0.86,im
GLNP_ECOLI,0.49,0.42,0.48,0.50,0.53,0.79,0.81,im
GLPT_ECOLI,0.31,0.50,0.48,0.50,0.57,0.84,0.85,im
GLTP_ECOLI,0.74,0.44,0.48,0.50,0.55,0.88,0.89,im
KDGL_ECOLI,0.33,0.45,0.48,0.50,0.45,0.88,0.89,im
KGTP_ECOLI,0.45,0.40,0.48,0.50,0.61,0.74,0.77,im
LACY_ECOLI,0.71,0.40,0.48,0.50,0.71,0.70,0.74,im
LGT_ECOLI,0.50,0.37,0.48,0.50,0.66,0.64,0.69,im
LLDP_ECOLI,0.66,0.53,0.48,0.50,0.59,0.66,0.66,im
LNT_ECOLI,0.60,0.61,0.48,0.50,0.54,0.67,0.71,im
LSPA_ECOLI,0.83,0.37,0.48,0.50,0.61,0.71,0.74,im
LYSP_ECOLI,0.34,0.51,0.48,0.50,0.67,0.90,0.90,im
MALF_ECOLI,0.63,0.54,0.48,0.50,0.65,0.79,0.81,im
MALG_ECOLI,0.70,0.40,0.48,0.50,0.56,0.86,0.83,im
MCP3_ECOLI,0.60,0.50,1.00,0.50,0.54,0.77,0.80,im
MSBB_ECOLI,0.16,0.51,0.48,0.50,0.33,0.39,0.48,im
MTR_ECOLI,0.74,0.70,0.48,0.50,0.66,0.65,0.69,im
NANT_ECOLI,0.20,0.46,0.48,0.50,0.57,0.78,0.81,im
NHAA_ECOLI,0.89,0.55,0.48,0.50,0.51,0.72,0.76,im
NHAB_ECOLI,0.70,0.46,0.48,0.50,0.56,0.78,0.73,im
PHEP_ECOLI,0.12,0.43,0.48,0.50,0.63,0.70,0.74,im
PHOR_ECOLI,0.61,0.52,0.48,0.50,0.54,0.67,0.52,im
PNTA_ECOLI,0.33,0.37,0.48,0.50,0.46,0.65,0.69,im
POTE_ECOLI,0.63,0.65,0.48,0.50,0.66,0.67,0.71,im
PROP_ECOLI,0.41,0.51,0.48,0.50,0.53,0.75,0.78,im
PSTA_ECOLI,0.34,0.67,0.48,0.50,0.52,0.76,0.79,im
PSTC_ECOLI,0.58,0.34,0.48,0.50,0.56,0.87,0.81,im
PTAA_ECOLI,0.59,0.56,0.48,0.50,0.55,0.80,0.82,im
PTBA_ECOLI,0.51,0.40,0.48,0.50,0.57,0.62,0.67,im
PTCC_ECOLI,0.50,0.57,0.48,0.50,0.71,0.61,0.66,im
PTDA_ECOLI,0.60,0.46,0.48,0.50,0.45,0.81,0.83,im
PTFB_ECOLI,0.37,0.47,0.48,0.50,0.39,0.76,0.79,im
PTGB_ECOLI,0.58,0.55,0.48,0.50,0.57,0.70,0.74,im
PTHB_ECOLI,0.36,0.47,0.48,0.50,0.51,0.69,0.72,im
PTMA_ECOLI,0.39,0.41,0.48,0.50,0.52,0.72,0.75,im
PTOA_ECOLI,0.35,0.51,0.48,0.50,0.61,0.71,0.74,im
PTTB_ECOLI,0.31,0.44,0.48,0.50,0.50,0.79,0.82,im
RODA_ECOLI,0.61,0.66,0.48,0.50,0.46,0.87,0.88,im
SECE_ECOLI,0.48,0.49,0.48,0.50,0.52,0.77,0.71,im
SECF_ECOLI,0.11,0.50,0.48,0.50,0.58,0.72,0.68,im
SECY_ECOLI,0.31,0.36,0.48,0.50,0.58,0.94,0.94,im
TNAB_ECOLI,0.68,0.51,0.48,0.50,0.71,0.75,0.78,im
XYLE_ECOLI,0.69,0.39,0.48,0.50,0.57,0.76,0.79,im
YCEE_ECOLI,0.52,0.54,0.48,0.50,0.62,0.76,0.79,im
EXBD_ECOLI,0.46,0.59,0.48,0.50,0.36,0.76,0.23,im
FTSL_ECOLI,0.36,0.45,0.48,0.50,0.38,0.79,0.17,im
FTSN_ECOLI,0.00,0.51,0.48,0.50,0.35,0.67,0.44,im
FTSQ_ECOLI,0.10,0.49,0.48,0.50,0.41,0.67,0.21,im
MOTB_ECOLI,0.30,0.51,0.48,0.50,0.42,0.61,0.34,im
TOLA_ECOLI,0.61,0.47,0.48,0.50,0.00,0.80,0.32,im
TOLQ_ECOLI,0.63,0.75,0.48,0.50,0.64,0.73,0.66,im
EMRB_ECOLI,0.71,0.52,0.48,0.50,0.64,1.00,0.99,im
ATKC_ECOLI,0.85,0.53,0.48,0.50,0.53,0.52,0.35,imS
NFRB_ECOLI,0.63,0.49,0.48,0.50,0.54,0.76,0.79,imS
NLPA_ECOLI,0.75,0.55,1.00,1.00,0.40,0.47,0.30,imL
CYOA_ECOLI,0.70,0.39,1.00,0.50,0.51,0.82,0.84,imL
ATKA_ECOLI,0.72,0.42,0.48,0.50,0.65,0.77,0.79,imU
BCR_ECOLI,0.79,0.41,0.48,0.50,0.66,0.81,0.83,imU
CADB_ECOLI,0.83,0.48,0.48,0.50,0.65,0.76,0.79,imU
CAIT_ECOLI,0.69,0.43,0.48,0.50,0.59,0.74,0.77,imU
CPXA_ECOLI,0.79,0.36,0.48,0.50,0.46,0.82,0.70,imU
CRED_ECOLI,0.78,0.33,0.48,0.50,0.57,0.77,0.79,imU
CYDB_ECOLI,0.75,0.37,0.48,0.50,0.64,0.70,0.74,imU
CYOB_ECOLI,0.59,0.29,0.48,0.50,0.64,0.75,0.77,imU
CYOE_ECOLI,0.67,0.37,0.48,0.50,0.54,0.64,0.68,imU
DMSC_ECOLI,0.66,0.48,0.48,0.50,0.54,0.70,0.74,imU
DPPB_ECOLI,0.64,0.46,0.48,0.50,0.48,0.73,0.76,imU
DSBD_ECOLI,0.76,0.71,0.48,0.50,0.50,0.71,0.75,imU
FEPD_ECOLI,0.84,0.49,0.48,0.50,0.55,0.78,0.74,imU
FEPG_ECOLI,0.77,0.55,0.48,0.50,0.51,0.78,0.74,imU
FTSH_ECOLI,0.81,0.44,0.48,0.50,0.42,0.67,0.68,imU
GLTS_ECOLI,0.58,0.60,0.48,0.50,0.59,0.73,0.76,imU
KEFC_ECOLI,0.63,0.42,0.48,0.50,0.48,0.77,0.80,imU
KUP_ECOLI,0.62,0.42,0.48,0.50,0.58,0.79,0.81,imU
MCP1_ECOLI,0.86,0.39,0.48,0.50,0.59,0.89,0.90,imU
MCP2_ECOLI,0.81,0.53,0.48,0.50,0.57,0.87,0.88,imU
MCP4_ECOLI,0.87,0.49,0.48,0.50,0.61,0.76,0.79,imU
MELB_ECOLI,0.47,0.46,0.48,0.50,0.62,0.74,0.77,imU
MOTA_ECOLI,0.76,0.41,0.48,0.50,0.50,0.59,0.62,imU
NUPC_ECOLI,0.70,0.53,0.48,0.50,0.70,0.86,0.87,imU
NUPG_ECOLI,0.64,0.45,0.48,0.50,0.67,0.61,0.66,imU
PNTB_ECOLI,0.81,0.52,0.48,0.50,0.57,0.78,0.80,imU
PTKC_ECOLI,0.73,0.26,0.48,0.50,0.57,0.75,0.78,imU
RHAT_ECOLI,0.49,0.61,1.00,0.50,0.56,0.71,0.74,imU
SECD_ECOLI,0.88,0.42,0.48,0.50,0.52,0.73,0.75,imU
SECG_ECOLI,0.84,0.54,0.48,0.50,0.75,0.92,0.70,imU
TEHA_ECOLI,0.63,0.51,0.48,0.50,0.64,0.72,0.76,imU
TYRP_ECOLI,0.86,0.55,0.48,0.50,0.63,0.81,0.83,imU
UHPB_ECOLI,0.79,0.54,0.48,0.50,0.50,0.66,0.68,imU
TONB_ECOLI,0.57,0.38,0.48,0.50,0.06,0.49,0.33,imU
LEP_ECOLI,0.78,0.44,0.48,0.50,0.45,0.73,0.68,imU
FADL_ECOLI,0.78,0.68,0.48,0.50,0.83,0.40,0.29,om
FHUA_ECOLI,0.63,0.69,0.48,0.50,0.65,0.41,0.28,om
LAMB_ECOLI,0.67,0.88,0.48,0.50,0.73,0.50,0.25,om
NFRA_ECOLI,0.61,0.75,0.48,0.50,0.51,0.33,0.33,om
NMPC_ECOLI,0.67,0.84,0.48,0.50,0.74,0.54,0.37,om
OMPA_ECOLI,0.74,0.90,0.48,0.50,0.57,0.53,0.29,om
OMPC_ECOLI,0.73,0.84,0.48,0.50,0.86,0.58,0.29,om
OMPF_ECOLI,0.75,0.76,0.48,0.50,0.83,0.57,0.30,om
OMPX_ECOLI,0.77,0.57,0.48,0.50,0.88,0.53,0.20,om
PHOE_ECOLI,0.74,0.78,0.48,0.50,0.75,0.54,0.15,om
TSX_ECOLI,0.68,0.76,0.48,0.50,0.84,0.45,0.27,om
BTUB_ECOLI,0.56,0.68,0.48,0.50,0.77,0.36,0.45,om
CIRA_ECOLI,0.65,0.51,0.48,0.50,0.66,0.54,0.33,om
FECA_ECOLI,0.52,0.81,0.48,0.50,0.72,0.38,0.38,om
FEPA_ECOLI,0.64,0.57,0.48,0.50,0.70,0.33,0.26,om
FHUE_ECOLI,0.60,0.76,1.00,0.50,0.77,0.59,0.52,om
OMPP_ECOLI,0.69,0.59,0.48,0.50,0.77,0.39,0.21,om
OMPT_ECOLI,0.63,0.49,0.48,0.50,0.79,0.45,0.28,om
TOLC_ECOLI,0.71,0.71,0.48,0.50,0.68,0.43,0.36,om
PA1_ECOLI,0.68,0.63,0.48,0.50,0.73,0.40,0.30,om
MULI_ECOLI,0.77,0.57,1.00,0.50,0.37,0.54,0.01,omL
NLPB_ECOLI,0.66,0.49,1.00,0.50,0.54,0.56,0.36,omL
NLPE_ECOLI,0.71,0.46,1.00,0.50,0.52,0.59,0.30,omL
PAL_ECOLI,0.67,0.55,1.00,0.50,0.66,0.58,0.16,omL
SLP_ECOLI,0.68,0.49,1.00,0.50,0.62,0.55,0.28,omL
AGP_ECOLI,0.74,0.49,0.48,0.50,0.42,0.54,0.36,pp
AMY1_ECOLI,0.70,0.61,0.48,0.50,0.56,0.52,0.43,pp
ARAF_ECOLI,0.66,0.86,0.48,0.50,0.34,0.41,0.36,pp
ASG2_ECOLI,0.73,0.78,0.48,0.50,0.58,0.51,0.31,pp
BGLX_ECOLI,0.65,0.57,0.48,0.50,0.47,0.47,0.51,pp
C562_ECOLI,0.72,0.86,0.48,0.50,0.17,0.55,0.21,pp
CN16_ECOLI,0.67,0.70,0.48,0.50,0.46,0.45,0.33,pp
CYPH_ECOLI,0.67,0.81,0.48,0.50,0.54,0.49,0.23,pp
CYSP_ECOLI,0.67,0.61,0.48,0.50,0.51,0.37,0.38,pp
DGAL_ECOLI,0.63,1.00,0.48,0.50,0.35,0.51,0.49,pp
DPPA_ECOLI,0.57,0.59,0.48,0.50,0.39,0.47,0.33,pp
DSBA_ECOLI,0.71,0.71,0.48,0.50,0.40,0.54,0.39,pp
DSBC_ECOLI,0.66,0.74,0.48,0.50,0.31,0.38,0.43,pp
ECOT_ECOLI,0.67,0.81,0.48,0.50,0.25,0.42,0.25,pp
ECPD_ECOLI,0.64,0.72,0.48,0.50,0.49,0.42,0.19,pp
FECB_ECOLI,0.68,0.82,0.48,0.50,0.38,0.65,0.56,pp
FECR_ECOLI,0.32,0.39,0.48,0.50,0.53,0.28,0.38,pp
FEPB_ECOLI,0.70,0.64,0.48,0.50,0.47,0.51,0.47,pp
FIMC_ECOLI,0.63,0.57,0.48,0.50,0.49,0.70,0.20,pp
GGT_ECOLI,0.74,0.82,0.48,0.50,0.49,0.49,0.41,pp
GLNH_ECOLI,0.63,0.86,0.48,0.50,0.39,0.47,0.34,pp
GLPQ_ECOLI,0.63,0.83,0.48,0.50,0.40,0.39,0.19,pp
HTRA_ECOLI,0.63,0.71,0.48,0.50,0.60,0.40,0.39,pp
LIVJ_ECOLI,0.71,0.86,0.48,0.50,0.40,0.54,0.32,pp
LIVK_ECOLI,0.68,0.78,0.48,0.50,0.43,0.44,0.42,pp
MALE_ECOLI,0.64,0.84,0.48,0.50,0.37,0.45,0.40,pp
MALM_ECOLI,0.74,0.47,0.48,0.50,0.50,0.57,0.42,pp
MEPA_ECOLI,0.75,0.84,0.48,0.50,0.35,0.52,0.33,pp
MODA_ECOLI,0.63,0.65,0.48,0.50,0.39,0.44,0.35,pp
NRFA_ECOLI,0.69,0.67,0.48,0.50,0.30,0.39,0.24,pp
NRFF_ECOLI,0.70,0.71,0.48,0.50,0.42,0.84,0.85,pp
OPPA_ECOLI,0.69,0.80,0.48,0.50,0.46,0.57,0.26,pp
OSMY_ECOLI,0.64,0.66,0.48,0.50,0.41,0.39,0.20,pp
POTD_ECOLI,0.63,0.80,0.48,0.50,0.46,0.31,0.29,pp
POTF_ECOLI,0.66,0.71,0.48,0.50,0.41,0.50,0.35,pp
PPA_ECOLI,0.69,0.59,0.48,0.50,0.46,0.44,0.52,pp
PPB_ECOLI,0.68,0.67,0.48,0.50,0.49,0.40,0.34,pp
PROX_ECOLI,0.64,0.78,0.48,0.50,0.50,0.36,0.38,pp
PSTS_ECOLI,0.62,0.78,0.48,0.50,0.47,0.49,0.54,pp
PTR_ECOLI,0.76,0.73,0.48,0.50,0.44,0.39,0.39,pp
RBSB_ECOLI,0.64,0.81,0.48,0.50,0.37,0.39,0.44,pp
SPEA_ECOLI,0.29,0.39,0.48,0.50,0.52,0.40,0.48,pp
SUBI_ECOLI,0.62,0.83,0.48,0.50,0.46,0.36,0.40,pp
TBPA_ECOLI,0.56,0.54,0.48,0.50,0.43,0.37,0.30,pp
TESA_ECOLI,0.69,0.66,0.48,0.50,0.41,0.50,0.25,pp
TOLB_ECOLI,0.69,0.65,0.48,0.50,0.63,0.48,0.41,pp
TORA_ECOLI,0.43,0.59,0.48,0.50,0.52,0.49,0.56,pp
TREA_ECOLI,0.74,0.56,0.48,0.50,0.47,0.68,0.30,pp
UGPB_ECOLI,0.71,0.57,0.48,0.50,0.48,0.35,0.32,pp
USHA_ECOLI,0.61,0.60,0.48,0.50,0.44,0.39,0.38,pp
XYLF_ECOLI,0.59,0.61,0.48,0.50,0.42,0.42,0.37,pp
YTFQ_ECOLI,0.74,0.74,0.48,0.50,0.31,0.53,0.52,pp
pytorch_widedeep/metrics.py
浏览文件 @ 8b4c3a8a
......@@ -38,10 +38,7 @@ class MultipleMetrics(object):
if isinstance(metric, Metric):
logs[self.prefix + metric._name] = metric(y_pred, y_true)
if isinstance(metric, TorchMetric):
if metric.num_classes == 2:
metric.update(torch.round(y_pred).int(), y_true.int())
if metric.num_classes > 2: # type: ignore[operator]
metric.update(torch.max(y_pred, dim=1).indices, y_true.int()) # type: ignore[attr-defined]
metric.update(y_pred, y_true.int()) # type: ignore[attr-defined]
logs[self.prefix + type(metric).__name__] = (
metric.compute().detach().cpu().numpy()
)
......
pytorch_widedeep/training/trainer.py
浏览文件 @ 8b4c3a8a
......@@ -149,8 +149,8 @@ class Trainer:
- List of objects of type :obj:`torchmetrics.Metric`. This can be any
metric from torchmetrics library `Examples
<https://torchmetrics.readthedocs.io/en/latest/references/modules.html#
classification-metrics>`_. This can also be a custom metric as
long as it is an object of type :obj:`Metric`. See `the instructions
classification-metrics>`_. This can also be a custom metric as long as
it is an object of type :obj:`Metric`. See `the instructions
<https://torchmetrics.readthedocs.io/en/latest/>`_.
class_weight: float, List or Tuple. optional. default=None
- float indicating the weight of the minority class in binary classification
......
pytorch_widedeep/version.py
浏览文件 @ 8b4c3a8a
__version__ = "1.0.13"
__version__ = "1.0.14"
tests/test_datasets/test_datasets.py
浏览文件 @ 8b4c3a8a
......@@ -2,7 +2,7 @@ import numpy as np
import pandas as pd
import pytest
from pytorch_widedeep.datasets import load_adult, load_bio_kdd04
from pytorch_widedeep.datasets import load_adult, load_bio_kdd04, load_ecoli
@pytest.mark.parametrize(
......@@ -33,3 +33,18 @@ def test_load_adult(as_frame):
assert (df.shape, type(df)) == ((48842, 15), pd.DataFrame)
else:
assert (df.shape, type(df)) == ((48842, 15), np.ndarray)
@pytest.mark.parametrize(
"as_frame",
[
(True),
(False),
],
)
def test_load_ecoli(as_frame):
df = load_ecoli(as_frame=as_frame)
if as_frame:
assert (df.shape, type(df)) == ((336, 9), pd.DataFrame)
else:
assert (df.shape, type(df)) == ((336, 9), np.ndarray)
tests/test_metrics/test_torchmetrics.py
浏览文件 @ 8b4c3a8a
......@@ -30,11 +30,11 @@ y_pred_bin_pt = torch.from_numpy(y_pred_bin_np)
@pytest.mark.parametrize(
"metric_name, sklearn_metric, torch_metric",
[
("Accuracy", accuracy_score, Accuracy(num_classes=2)),
("Precision", precision_score, Precision(num_classes=2, average="none")),
("Recall", recall_score, Recall(num_classes=2, average="none")),
("F1", f1_score, F1(num_classes=2, average="none")),
("FBeta", f2_score_bin, FBeta(beta=2, num_classes=2, average="none")),
("Accuracy", accuracy_score, Accuracy()),
("Precision", precision_score, Precision()),
("Recall", recall_score, Recall()),
("F1", f1_score, F1()),
("FBeta", f2_score_bin, FBeta(beta=2)),
],
)
def test_binary_metrics(metric_name, sklearn_metric, torch_metric):
......@@ -58,8 +58,8 @@ y_pred_muli_np = np.array(
[0.1, 0.1, 0.8],
[0.1, 0.6, 0.3],
[0.1, 0.8, 0.1],
[0.1, 0.6, 0.6],
[0.2, 0.6, 0.8],
[0.1, 0.3, 0.6],
[0.1, 0.1, 0.8],
[0.6, 0.1, 0.3],
[0.7, 0.2, 0.1],
[0.1, 0.7, 0.2],
......
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