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b2534ba05e ... 168a6350b3

Author SHA1 Message Date
Aleksey Filippov
168a6350b3 Add additional systems (temp + density / viscosity) 2025-02-26 14:12:30 +04:00
Aleksey Filippov
f576f28814 Rename cardio_fuzzy to cardio_tree 2025-02-26 14:12:21 +04:00
Aleksey Filippov
5198e126d0 Remove unused code 2025-02-26 14:12:07 +04:00
Aleksey Filippov
602d36681d Sync rules alg with paper (remove clusters based groups) 2025-02-26 14:11:46 +04:00
10 changed files with 5540 additions and 2011 deletions
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1073
cardio_fuzzy.ipynb → cardio_tree.ipynb
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data/temp_density_tree.model.sav Normal file
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data/temp_viscosity_tree.model.sav Normal file
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1041
density_tree.ipynb
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39
src/rules.py
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@ -288,6 +288,26 @@ def simplify_and_group_rules(
return new_rules
def simplify_rules(X: pd.DataFrame, rules: List[Rule]) -> List[Rule]:
minmax = _get_variables_minmax(X)
new_rules: List[Rule] = []
for rule in rules:
intervals = _get_varibles_interval(rule.get_antecedent())
new_atoms = []
for key, value in intervals.items():
val: float = 0
if value[0] is None and value[1] is not None:
val = minmax[key][0]
if value[1] is None and value[0] is not None:
val = minmax[key][1]
if value[0] is not None and value[1] is not None:
val = (value[0] + value[1]) / 2
new_atoms.append(RuleAtom(key, ComparisonType.EQUALS, val))
new_rules.append(Rule(new_atoms, rule.get_consequent()))
return new_rules
def _get_fuzzy_rule_atom(
fuzzy_variable: FuzzyVariable, value: float
) -> Tuple[Term, float]:
@ -328,11 +348,11 @@ def _get_fuzzy_rules(
def _delete_same_fuzzy_rules(
rules_cluster: List[Tuple[List[RuleAtom], Term, float]]
rules: List[Tuple[List[RuleAtom], Term, float]]
) -> List[Tuple[List[RuleAtom], Term, float]]:
same_rules: List[int] = []
for rule1_index, rule1 in enumerate(rules_cluster):
for rule2_index, rule2 in enumerate(rules_cluster):
for rule1_index, rule1 in enumerate(rules):
for rule2_index, rule2 in enumerate(rules):
if rule1_index >= rule2_index:
continue
# Remove the same rules
@ -347,10 +367,10 @@ def _delete_same_fuzzy_rules(
if str(rule1[0]) == str(rule2[0]) and str(rule1[2]) > str(rule2[2]):
same_rules.append(rule1_index)
break
return [rule for index, rule in enumerate(rules_cluster) if index not in same_rules]
return [rule for index, rule in enumerate(rules) if index not in same_rules]
def get_fuzzy_rules(
def get_grouped_fuzzy_rules(
clustered_rules: List[List[Rule]], fuzzy_variables: Dict[str, FuzzyVariable]
) -> List[FuzzyRule]:
fuzzy_rules: List[List[Tuple[List[RuleAtom], Term, float]]] = []
@ -363,3 +383,12 @@ def get_fuzzy_rules(
for cluster in fuzzy_rules
for item in cluster
]
def get_fuzzy_rules(
rules: List[Rule], fuzzy_variables: Dict[str, FuzzyVariable]
) -> List[FuzzyRule]:
fuzzy_rules: List[Tuple[List[RuleAtom], Term, float]] = []
fuzzy_rules = _get_fuzzy_rules(rules, fuzzy_variables)
fuzzy_rules = _delete_same_fuzzy_rules(fuzzy_rules)
return [FuzzyRule(reduce(and_, item[0]), item[1]) for item in fuzzy_rules]

773
temp_density_regression.ipynb Normal file
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@ -0,0 +1,773 @@
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"source": [
"import pandas as pd\n",
"\n",
"train = pd.read_csv(\"data/density_train.csv\", sep=\";\", decimal=\",\")\n",
"test = pd.read_csv(\"data/density_test.csv\", sep=\";\", decimal=\",\")\n",
"\n",
"display(train.head())\n",
"display(test.head())"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
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"source": [
"y_train = train[\"T\"]\n",
"X_train = train.drop([\"T\"], axis=1)\n",
"\n",
"display(X_train.head())\n",
"display(y_train.head())\n",
"\n",
"y_test = test[\"T\"]\n",
"X_test = test.drop([\"T\"], axis=1)\n",
"\n",
"display(X_test.head())\n",
"display(y_test.head())"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.pipeline import make_pipeline\n",
"from sklearn.preprocessing import PolynomialFeatures\n",
"from sklearn import linear_model, tree, neighbors, ensemble\n",
"\n",
"random_state = 9\n",
"\n",
"models = {\n",
" \"linear\": {\"model\": linear_model.LinearRegression(n_jobs=-1)},\n",
" \"linear_poly\": {\n",
" \"model\": make_pipeline(\n",
" PolynomialFeatures(degree=2),\n",
" linear_model.LinearRegression(fit_intercept=False, n_jobs=-1),\n",
" )\n",
" },\n",
" \"linear_interact\": {\n",
" \"model\": make_pipeline(\n",
" PolynomialFeatures(interaction_only=True),\n",
" linear_model.LinearRegression(fit_intercept=False, n_jobs=-1),\n",
" )\n",
" },\n",
" \"ridge\": {\"model\": linear_model.RidgeCV()},\n",
" \"decision_tree\": {\n",
" \"model\": tree.DecisionTreeRegressor(random_state=random_state, max_depth=6, criterion=\"absolute_error\")\n",
" },\n",
" \"knn\": {\"model\": neighbors.KNeighborsRegressor(n_neighbors=7, n_jobs=-1)},\n",
" \"random_forest\": {\n",
" \"model\": ensemble.RandomForestRegressor(\n",
" max_depth=7, random_state=random_state, n_jobs=-1\n",
" )\n",
" },\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Model: linear\n",
"Model: linear_poly\n",
"Model: linear_interact\n",
"Model: ridge\n",
"Model: decision_tree\n",
"Model: knn\n",
"Model: random_forest\n"
]
}
],
"source": [
"import math\n",
"from sklearn import metrics\n",
"\n",
"for model_name in models.keys():\n",
" print(f\"Model: {model_name}\")\n",
" fitted_model = models[model_name][\"model\"].fit(\n",
" X_train.values, y_train.values.ravel()\n",
" )\n",
" y_train_pred = fitted_model.predict(X_train.values)\n",
" y_test_pred = fitted_model.predict(X_test.values)\n",
" models[model_name][\"fitted\"] = fitted_model\n",
" models[model_name][\"MSE_train\"] = metrics.mean_squared_error(y_train, y_train_pred)\n",
" models[model_name][\"MSE_test\"] = metrics.mean_squared_error(y_test, y_test_pred)\n",
" models[model_name][\"MAE_train\"] = metrics.mean_absolute_error(y_train, y_train_pred)\n",
" models[model_name][\"MAE_test\"] = metrics.mean_absolute_error(y_test, y_test_pred)\n",
" models[model_name][\"R2_train\"] = metrics.r2_score(y_train, y_train_pred)\n",
" models[model_name][\"R2_test\"] = metrics.r2_score(y_test, y_test_pred)"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
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" </tr>\n",
" <tr>\n",
" <th id=\"T_2421b_level0_row1\" class=\"row_heading level0 row1\" >linear_interact</th>\n",
" <td id=\"T_2421b_row1_col0\" class=\"data row1 col0\" >9.693323</td>\n",
" <td id=\"T_2421b_row1_col1\" class=\"data row1 col1\" >10.875442</td>\n",
" <td id=\"T_2421b_row1_col2\" class=\"data row1 col2\" >2.544944</td>\n",
" <td id=\"T_2421b_row1_col3\" class=\"data row1 col3\" >2.718424</td>\n",
" <td id=\"T_2421b_row1_col4\" class=\"data row1 col4\" >0.963492</td>\n",
" <td id=\"T_2421b_row1_col5\" class=\"data row1 col5\" >0.949019</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_2421b_level0_row2\" class=\"row_heading level0 row2\" >linear</th>\n",
" <td id=\"T_2421b_row2_col0\" class=\"data row2 col0\" >10.468503</td>\n",
" <td id=\"T_2421b_row2_col1\" class=\"data row2 col1\" >14.820315</td>\n",
" <td id=\"T_2421b_row2_col2\" class=\"data row2 col2\" >2.657476</td>\n",
" <td id=\"T_2421b_row2_col3\" class=\"data row2 col3\" >2.930229</td>\n",
" <td id=\"T_2421b_row2_col4\" class=\"data row2 col4\" >0.960572</td>\n",
" <td id=\"T_2421b_row2_col5\" class=\"data row2 col5\" >0.930526</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_2421b_level0_row3\" class=\"row_heading level0 row3\" >decision_tree</th>\n",
" <td id=\"T_2421b_row3_col0\" class=\"data row3 col0\" >10.526316</td>\n",
" <td id=\"T_2421b_row3_col1\" class=\"data row3 col1\" >47.426471</td>\n",
" <td id=\"T_2421b_row3_col2\" class=\"data row3 col2\" >1.842105</td>\n",
" <td id=\"T_2421b_row3_col3\" class=\"data row3 col3\" >5.735294</td>\n",
" <td id=\"T_2421b_row3_col4\" class=\"data row3 col4\" >0.960355</td>\n",
" <td id=\"T_2421b_row3_col5\" class=\"data row3 col5\" >0.777676</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_2421b_level0_row4\" class=\"row_heading level0 row4\" >random_forest</th>\n",
" <td id=\"T_2421b_row4_col0\" class=\"data row4 col0\" >20.243876</td>\n",
" <td id=\"T_2421b_row4_col1\" class=\"data row4 col1\" >54.501240</td>\n",
" <td id=\"T_2421b_row4_col2\" class=\"data row4 col2\" >3.592953</td>\n",
" <td id=\"T_2421b_row4_col3\" class=\"data row4 col3\" >6.598133</td>\n",
" <td id=\"T_2421b_row4_col4\" class=\"data row4 col4\" >0.923755</td>\n",
" <td id=\"T_2421b_row4_col5\" class=\"data row4 col5\" >0.744512</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_2421b_level0_row5\" class=\"row_heading level0 row5\" >knn</th>\n",
" <td id=\"T_2421b_row5_col0\" class=\"data row5 col0\" >174.100430</td>\n",
" <td id=\"T_2421b_row5_col1\" class=\"data row5 col1\" >191.176471</td>\n",
" <td id=\"T_2421b_row5_col2\" class=\"data row5 col2\" >10.808271</td>\n",
" <td id=\"T_2421b_row5_col3\" class=\"data row5 col3\" >11.680672</td>\n",
" <td id=\"T_2421b_row5_col4\" class=\"data row5 col4\" >0.344285</td>\n",
" <td id=\"T_2421b_row5_col5\" class=\"data row5 col5\" >0.103812</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_2421b_level0_row6\" class=\"row_heading level0 row6\" >ridge</th>\n",
" <td id=\"T_2421b_row6_col0\" class=\"data row6 col0\" >243.364664</td>\n",
" <td id=\"T_2421b_row6_col1\" class=\"data row6 col1\" >199.601477</td>\n",
" <td id=\"T_2421b_row6_col2\" class=\"data row6 col2\" >13.472724</td>\n",
" <td id=\"T_2421b_row6_col3\" class=\"data row6 col3\" >12.396799</td>\n",
" <td id=\"T_2421b_row6_col4\" class=\"data row6 col4\" >0.083415</td>\n",
" <td id=\"T_2421b_row6_col5\" class=\"data row6 col5\" >0.064317</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n"
],
"text/plain": [
"<pandas.io.formats.style.Styler at 0x16867d550>"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"reg_metrics = pd.DataFrame.from_dict(models, \"index\")[\n",
" [\"MSE_train\", \"MSE_test\", \"MAE_train\", \"MAE_test\", \"R2_train\", \"R2_test\"]\n",
"]\n",
"reg_metrics.sort_values(by=\"MAE_test\").style.background_gradient(\n",
" cmap=\"viridis\", low=1, high=0.3, subset=[\"MSE_train\", \"MSE_test\"]\n",
").background_gradient(cmap=\"plasma\", low=0.3, high=1, subset=[\"MAE_test\", \"R2_test\"])"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"|--- Density <= 1.04\n",
"| |--- Density <= 1.03\n",
"| | |--- value: [70.00]\n",
"| |--- Density > 1.03\n",
"| | |--- Density <= 1.04\n",
"| | | |--- value: [65.00]\n",
"| | |--- Density > 1.04\n",
"| | | |--- value: [60.00]\n",
"|--- Density > 1.04\n",
"| |--- Density <= 1.07\n",
"| | |--- TiO2 <= 0.03\n",
"| | | |--- Al2O3 <= 0.03\n",
"| | | | |--- Density <= 1.05\n",
"| | | | | |--- Density <= 1.05\n",
"| | | | | | |--- value: [50.00]\n",
"| | | | | |--- Density > 1.05\n",
"| | | | | | |--- value: [42.50]\n",
"| | | | |--- Density > 1.05\n",
"| | | | | |--- Density <= 1.06\n",
"| | | | | | |--- value: [35.00]\n",
"| | | | | |--- Density > 1.06\n",
"| | | | | | |--- value: [22.50]\n",
"| | | |--- Al2O3 > 0.03\n",
"| | | | |--- Density <= 1.06\n",
"| | | | | |--- Density <= 1.05\n",
"| | | | | | |--- value: [70.00]\n",
"| | | | | |--- Density > 1.05\n",
"| | | | | | |--- value: [65.00]\n",
"| | | | |--- Density > 1.06\n",
"| | | | | |--- Density <= 1.07\n",
"| | | | | | |--- value: [55.00]\n",
"| | | | | |--- Density > 1.07\n",
"| | | | | | |--- value: [50.00]\n",
"| | |--- TiO2 > 0.03\n",
"| | | |--- Density <= 1.06\n",
"| | | | |--- value: [70.00]\n",
"| | | |--- Density > 1.06\n",
"| | | | |--- Density <= 1.06\n",
"| | | | | |--- value: [65.00]\n",
"| | | | |--- Density > 1.06\n",
"| | | | | |--- value: [60.00]\n",
"| |--- Density > 1.07\n",
"| | |--- Density <= 1.12\n",
"| | | |--- Density <= 1.08\n",
"| | | | |--- Density <= 1.07\n",
"| | | | | |--- value: [45.00]\n",
"| | | | |--- Density > 1.07\n",
"| | | | | |--- Density <= 1.08\n",
"| | | | | | |--- value: [40.00]\n",
"| | | | | |--- Density > 1.08\n",
"| | | | | | |--- value: [35.00]\n",
"| | | |--- Density > 1.08\n",
"| | | | |--- Density <= 1.09\n",
"| | | | | |--- value: [30.00]\n",
"| | | | |--- Density > 1.09\n",
"| | | | | |--- Al2O3 <= 0.03\n",
"| | | | | | |--- value: [22.50]\n",
"| | | | | |--- Al2O3 > 0.03\n",
"| | | | | | |--- value: [20.00]\n",
"| | |--- Density > 1.12\n",
"| | | |--- Density <= 1.18\n",
"| | | | |--- Density <= 1.15\n",
"| | | | | |--- value: [70.00]\n",
"| | | | |--- Density > 1.15\n",
"| | | | | |--- Al2O3 <= 0.15\n",
"| | | | | | |--- value: [65.00]\n",
"| | | | | |--- Al2O3 > 0.15\n",
"| | | | | | |--- value: [50.00]\n",
"| | | |--- Density > 1.18\n",
"| | | | |--- Al2O3 <= 0.15\n",
"| | | | | |--- Density <= 1.20\n",
"| | | | | | |--- value: [50.00]\n",
"| | | | | |--- Density > 1.20\n",
"| | | | | | |--- value: [30.00]\n",
"| | | | |--- Al2O3 > 0.15\n",
"| | | | | |--- Density <= 1.18\n",
"| | | | | | |--- value: [30.00]\n",
"| | | | | |--- Density > 1.18\n",
"| | | | | | |--- value: [22.50]\n",
"\n"
]
}
],
"source": [
"model = models[\"decision_tree\"][\"fitted\"]\n",
"rules = tree.export_text(\n",
" model, feature_names=X_train.columns.values.tolist()\n",
")\n",
"print(rules)"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [],
"source": [
"import pickle\n",
"\n",
"pickle.dump(model, open(\"data/temp_density_tree.model.sav\", \"wb\"))"
]
}
],
"metadata": {
"kernelspec": {
"display_name": ".venv",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.9"
}
},
"nbformat": 4,
"nbformat_minor": 2
}

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{
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"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>T</th>\n",
" <th>Al2O3</th>\n",
" <th>TiO2</th>\n",
" <th>Viscosity</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>20</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>3.707</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>25</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>3.180</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>35</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>2.361</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>45</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.832</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>50</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.629</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" T Al2O3 TiO2 Viscosity\n",
"0 20 0.0 0.0 3.707\n",
"1 25 0.0 0.0 3.180\n",
"2 35 0.0 0.0 2.361\n",
"3 45 0.0 0.0 1.832\n",
"4 50 0.0 0.0 1.629"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"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>T</th>\n",
" <th>Al2O3</th>\n",
" <th>TiO2</th>\n",
" <th>Viscosity</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>30</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>2.716</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>40</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>2.073</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>60</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>1.329</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>65</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>1.211</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>25</td>\n",
" <td>0.05</td>\n",
" <td>0.0</td>\n",
" <td>4.120</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" T Al2O3 TiO2 Viscosity\n",
"0 30 0.00 0.0 2.716\n",
"1 40 0.00 0.0 2.073\n",
"2 60 0.00 0.0 1.329\n",
"3 65 0.00 0.0 1.211\n",
"4 25 0.05 0.0 4.120"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import pandas as pd\n",
"\n",
"train = pd.read_csv(\"data/viscosity_train.csv\", sep=\";\", decimal=\",\")\n",
"test = pd.read_csv(\"data/viscosity_test.csv\", sep=\";\", decimal=\",\")\n",
"\n",
"display(train.head())\n",
"display(test.head())"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
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" 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>Al2O3</th>\n",
" <th>TiO2</th>\n",
" <th>Viscosity</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>3.707</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>3.180</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>2.361</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.832</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.629</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Al2O3 TiO2 Viscosity\n",
"0 0.0 0.0 3.707\n",
"1 0.0 0.0 3.180\n",
"2 0.0 0.0 2.361\n",
"3 0.0 0.0 1.832\n",
"4 0.0 0.0 1.629"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"0 20\n",
"1 25\n",
"2 35\n",
"3 45\n",
"4 50\n",
"Name: T, dtype: int64"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"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>Al2O3</th>\n",
" <th>TiO2</th>\n",
" <th>Viscosity</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>2.716</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>2.073</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>1.329</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>1.211</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0.05</td>\n",
" <td>0.0</td>\n",
" <td>4.120</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Al2O3 TiO2 Viscosity\n",
"0 0.00 0.0 2.716\n",
"1 0.00 0.0 2.073\n",
"2 0.00 0.0 1.329\n",
"3 0.00 0.0 1.211\n",
"4 0.05 0.0 4.120"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"0 30\n",
"1 40\n",
"2 60\n",
"3 65\n",
"4 25\n",
"Name: T, dtype: int64"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"y_train = train[\"T\"]\n",
"X_train = train.drop([\"T\"], axis=1)\n",
"\n",
"display(X_train.head())\n",
"display(y_train.head())\n",
"\n",
"y_test = test[\"T\"]\n",
"X_test = test.drop([\"T\"], axis=1)\n",
"\n",
"display(X_test.head())\n",
"display(y_test.head())"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.pipeline import make_pipeline\n",
"from sklearn.preprocessing import PolynomialFeatures\n",
"from sklearn import linear_model, tree, neighbors, ensemble\n",
"\n",
"random_state = 9\n",
"\n",
"models = {\n",
" \"linear\": {\"model\": linear_model.LinearRegression(n_jobs=-1)},\n",
" \"linear_poly\": {\n",
" \"model\": make_pipeline(\n",
" PolynomialFeatures(degree=2),\n",
" linear_model.LinearRegression(fit_intercept=False, n_jobs=-1),\n",
" )\n",
" },\n",
" \"linear_interact\": {\n",
" \"model\": make_pipeline(\n",
" PolynomialFeatures(interaction_only=True),\n",
" linear_model.LinearRegression(fit_intercept=False, n_jobs=-1),\n",
" )\n",
" },\n",
" \"ridge\": {\"model\": linear_model.RidgeCV()},\n",
" \"decision_tree\": {\n",
" \"model\": tree.DecisionTreeRegressor(random_state=random_state, max_depth=6, criterion=\"absolute_error\")\n",
" },\n",
" \"knn\": {\"model\": neighbors.KNeighborsRegressor(n_neighbors=7, n_jobs=-1)},\n",
" \"random_forest\": {\n",
" \"model\": ensemble.RandomForestRegressor(\n",
" max_depth=7, random_state=random_state, n_jobs=-1\n",
" )\n",
" },\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Model: linear\n",
"Model: linear_poly\n",
"Model: linear_interact\n",
"Model: ridge\n",
"Model: decision_tree\n",
"Model: knn\n",
"Model: random_forest\n"
]
}
],
"source": [
"import math\n",
"from sklearn import metrics\n",
"\n",
"for model_name in models.keys():\n",
" print(f\"Model: {model_name}\")\n",
" fitted_model = models[model_name][\"model\"].fit(\n",
" X_train.values, y_train.values.ravel()\n",
" )\n",
" y_train_pred = fitted_model.predict(X_train.values)\n",
" y_test_pred = fitted_model.predict(X_test.values)\n",
" models[model_name][\"fitted\"] = fitted_model\n",
" models[model_name][\"MSE_train\"] = metrics.mean_squared_error(y_train, y_train_pred)\n",
" models[model_name][\"MSE_test\"] = metrics.mean_squared_error(y_test, y_test_pred)\n",
" models[model_name][\"MAE_train\"] = metrics.mean_absolute_error(y_train, y_train_pred)\n",
" models[model_name][\"MAE_test\"] = metrics.mean_absolute_error(y_test, y_test_pred)\n",
" models[model_name][\"R2_train\"] = metrics.r2_score(y_train, y_train_pred)\n",
" models[model_name][\"R2_test\"] = metrics.r2_score(y_test, y_test_pred)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
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" background-color: #54c568;\n",
" color: #000000;\n",
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" background-color: #c5407e;\n",
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"#T_6c8fb_row4_col5, #T_6c8fb_row5_col5 {\n",
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"}\n",
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" background-color: #cd4a76;\n",
" color: #f1f1f1;\n",
"}\n",
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" background-color: #a8db34;\n",
" color: #000000;\n",
"}\n",
"</style>\n",
"<table id=\"T_6c8fb\">\n",
" <thead>\n",
" <tr>\n",
" <th class=\"blank level0\" >&nbsp;</th>\n",
" <th id=\"T_6c8fb_level0_col0\" class=\"col_heading level0 col0\" >MSE_train</th>\n",
" <th id=\"T_6c8fb_level0_col1\" class=\"col_heading level0 col1\" >MSE_test</th>\n",
" <th id=\"T_6c8fb_level0_col2\" class=\"col_heading level0 col2\" >MAE_train</th>\n",
" <th id=\"T_6c8fb_level0_col3\" class=\"col_heading level0 col3\" >MAE_test</th>\n",
" <th id=\"T_6c8fb_level0_col4\" class=\"col_heading level0 col4\" >R2_train</th>\n",
" <th id=\"T_6c8fb_level0_col5\" class=\"col_heading level0 col5\" >R2_test</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th id=\"T_6c8fb_level0_row0\" class=\"row_heading level0 row0\" >linear_poly</th>\n",
" <td id=\"T_6c8fb_row0_col0\" class=\"data row0 col0\" >4.827768</td>\n",
" <td id=\"T_6c8fb_row0_col1\" class=\"data row0 col1\" >4.877296</td>\n",
" <td id=\"T_6c8fb_row0_col2\" class=\"data row0 col2\" >1.522643</td>\n",
" <td id=\"T_6c8fb_row0_col3\" class=\"data row0 col3\" >1.743058</td>\n",
" <td id=\"T_6c8fb_row0_col4\" class=\"data row0 col4\" >0.980864</td>\n",
" <td id=\"T_6c8fb_row0_col5\" class=\"data row0 col5\" >0.974964</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_6c8fb_level0_row1\" class=\"row_heading level0 row1\" >linear_interact</th>\n",
" <td id=\"T_6c8fb_row1_col0\" class=\"data row1 col0\" >21.786348</td>\n",
" <td id=\"T_6c8fb_row1_col1\" class=\"data row1 col1\" >23.459572</td>\n",
" <td id=\"T_6c8fb_row1_col2\" class=\"data row1 col2\" >3.830996</td>\n",
" <td id=\"T_6c8fb_row1_col3\" class=\"data row1 col3\" >4.381115</td>\n",
" <td id=\"T_6c8fb_row1_col4\" class=\"data row1 col4\" >0.913644</td>\n",
" <td id=\"T_6c8fb_row1_col5\" class=\"data row1 col5\" >0.879577</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_6c8fb_level0_row2\" class=\"row_heading level0 row2\" >linear</th>\n",
" <td id=\"T_6c8fb_row2_col0\" class=\"data row2 col0\" >27.766510</td>\n",
" <td id=\"T_6c8fb_row2_col1\" class=\"data row2 col1\" >35.430313</td>\n",
" <td id=\"T_6c8fb_row2_col2\" class=\"data row2 col2\" >4.088006</td>\n",
" <td id=\"T_6c8fb_row2_col3\" class=\"data row2 col3\" >5.106782</td>\n",
" <td id=\"T_6c8fb_row2_col4\" class=\"data row2 col4\" >0.889940</td>\n",
" <td id=\"T_6c8fb_row2_col5\" class=\"data row2 col5\" >0.818129</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_6c8fb_level0_row3\" class=\"row_heading level0 row3\" >ridge</th>\n",
" <td id=\"T_6c8fb_row3_col0\" class=\"data row3 col0\" >31.827476</td>\n",
" <td id=\"T_6c8fb_row3_col1\" class=\"data row3 col1\" >36.230606</td>\n",
" <td id=\"T_6c8fb_row3_col2\" class=\"data row3 col2\" >4.383008</td>\n",
" <td id=\"T_6c8fb_row3_col3\" class=\"data row3 col3\" >5.226480</td>\n",
" <td id=\"T_6c8fb_row3_col4\" class=\"data row3 col4\" >0.873843</td>\n",
" <td id=\"T_6c8fb_row3_col5\" class=\"data row3 col5\" >0.814021</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_6c8fb_level0_row4\" class=\"row_heading level0 row4\" >random_forest</th>\n",
" <td id=\"T_6c8fb_row4_col0\" class=\"data row4 col0\" >8.525285</td>\n",
" <td id=\"T_6c8fb_row4_col1\" class=\"data row4 col1\" >45.444651</td>\n",
" <td id=\"T_6c8fb_row4_col2\" class=\"data row4 col2\" >2.542935</td>\n",
" <td id=\"T_6c8fb_row4_col3\" class=\"data row4 col3\" >5.749510</td>\n",
" <td id=\"T_6c8fb_row4_col4\" class=\"data row4 col4\" >0.966208</td>\n",
" <td id=\"T_6c8fb_row4_col5\" class=\"data row4 col5\" >0.766723</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_6c8fb_level0_row5\" class=\"row_heading level0 row5\" >decision_tree</th>\n",
" <td id=\"T_6c8fb_row5_col0\" class=\"data row5 col0\" >3.289474</td>\n",
" <td id=\"T_6c8fb_row5_col1\" class=\"data row5 col1\" >45.588235</td>\n",
" <td id=\"T_6c8fb_row5_col2\" class=\"data row5 col2\" >0.921053</td>\n",
" <td id=\"T_6c8fb_row5_col3\" class=\"data row5 col3\" >6.176471</td>\n",
" <td id=\"T_6c8fb_row5_col4\" class=\"data row5 col4\" >0.986961</td>\n",
" <td id=\"T_6c8fb_row5_col5\" class=\"data row5 col5\" >0.765986</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_6c8fb_level0_row6\" class=\"row_heading level0 row6\" >knn</th>\n",
" <td id=\"T_6c8fb_row6_col0\" class=\"data row6 col0\" >61.855532</td>\n",
" <td id=\"T_6c8fb_row6_col1\" class=\"data row6 col1\" >64.165666</td>\n",
" <td id=\"T_6c8fb_row6_col2\" class=\"data row6 col2\" >6.522556</td>\n",
" <td id=\"T_6c8fb_row6_col3\" class=\"data row6 col3\" >6.806723</td>\n",
" <td id=\"T_6c8fb_row6_col4\" class=\"data row6 col4\" >0.754819</td>\n",
" <td id=\"T_6c8fb_row6_col5\" class=\"data row6 col5\" >0.670624</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n"
],
"text/plain": [
"<pandas.io.formats.style.Styler at 0x11b747260>"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"reg_metrics = pd.DataFrame.from_dict(models, \"index\")[\n",
" [\"MSE_train\", \"MSE_test\", \"MAE_train\", \"MAE_test\", \"R2_train\", \"R2_test\"]\n",
"]\n",
"reg_metrics.sort_values(by=\"MAE_test\").style.background_gradient(\n",
" cmap=\"viridis\", low=1, high=0.3, subset=[\"MSE_train\", \"MSE_test\"]\n",
").background_gradient(cmap=\"plasma\", low=0.3, high=1, subset=[\"MAE_test\", \"R2_test\"])"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"|--- Viscosity <= 2.86\n",
"| |--- Viscosity <= 1.38\n",
"| | |--- value: [70.00]\n",
"| |--- Viscosity > 1.38\n",
"| | |--- Viscosity <= 1.78\n",
"| | | |--- Viscosity <= 1.72\n",
"| | | | |--- TiO2 <= 0.03\n",
"| | | | | |--- Al2O3 <= 0.03\n",
"| | | | | | |--- value: [52.50]\n",
"| | | | | |--- Al2O3 > 0.03\n",
"| | | | | | |--- value: [57.50]\n",
"| | | | |--- TiO2 > 0.03\n",
"| | | | | |--- value: [60.00]\n",
"| | | |--- Viscosity > 1.72\n",
"| | | | |--- value: [70.00]\n",
"| | |--- Viscosity > 1.78\n",
"| | | |--- TiO2 <= 0.18\n",
"| | | | |--- Al2O3 <= 0.18\n",
"| | | | | |--- Viscosity <= 2.24\n",
"| | | | | | |--- value: [50.00]\n",
"| | | | | |--- Viscosity > 2.24\n",
"| | | | | | |--- value: [40.00]\n",
"| | | | |--- Al2O3 > 0.18\n",
"| | | | | |--- Viscosity <= 2.29\n",
"| | | | | | |--- value: [60.00]\n",
"| | | | | |--- Viscosity > 2.29\n",
"| | | | | | |--- value: [55.00]\n",
"| | | |--- TiO2 > 0.18\n",
"| | | | |--- Viscosity <= 2.22\n",
"| | | | | |--- value: [70.00]\n",
"| | | | |--- Viscosity > 2.22\n",
"| | | | | |--- Viscosity <= 2.69\n",
"| | | | | | |--- value: [60.00]\n",
"| | | | | |--- Viscosity > 2.69\n",
"| | | | | | |--- value: [55.00]\n",
"|--- Viscosity > 2.86\n",
"| |--- Viscosity <= 3.64\n",
"| | |--- TiO2 <= 0.18\n",
"| | | |--- Viscosity <= 3.15\n",
"| | | | |--- value: [35.00]\n",
"| | | |--- Viscosity > 3.15\n",
"| | | | |--- Viscosity <= 3.47\n",
"| | | | | |--- Al2O3 <= 0.03\n",
"| | | | | | |--- value: [25.00]\n",
"| | | | | |--- Al2O3 > 0.03\n",
"| | | | | | |--- value: [30.00]\n",
"| | | | |--- Viscosity > 3.47\n",
"| | | | | |--- value: [40.00]\n",
"| | |--- TiO2 > 0.18\n",
"| | | |--- value: [45.00]\n",
"| |--- Viscosity > 3.64\n",
"| | |--- Viscosity <= 6.27\n",
"| | | |--- TiO2 <= 0.18\n",
"| | | | |--- Al2O3 <= 0.18\n",
"| | | | | |--- TiO2 <= 0.03\n",
"| | | | | | |--- value: [20.00]\n",
"| | | | | |--- TiO2 > 0.03\n",
"| | | | | | |--- value: [22.50]\n",
"| | | | |--- Al2O3 > 0.18\n",
"| | | | | |--- Viscosity <= 4.42\n",
"| | | | | | |--- value: [35.00]\n",
"| | | | | |--- Viscosity > 4.42\n",
"| | | | | | |--- value: [27.50]\n",
"| | | |--- TiO2 > 0.18\n",
"| | | | |--- Viscosity <= 4.65\n",
"| | | | | |--- value: [35.00]\n",
"| | | | |--- Viscosity > 4.65\n",
"| | | | | |--- Viscosity <= 5.40\n",
"| | | | | | |--- value: [30.00]\n",
"| | | | | |--- Viscosity > 5.40\n",
"| | | | | | |--- value: [25.00]\n",
"| | |--- Viscosity > 6.27\n",
"| | | |--- value: [20.00]\n",
"\n"
]
}
],
"source": [
"model = models[\"decision_tree\"][\"fitted\"]\n",
"rules = tree.export_text(\n",
" model, feature_names=X_train.columns.values.tolist()\n",
")\n",
"print(rules)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"import pickle\n",
"\n",
"pickle.dump(model, open(\"data/temp_viscosity_tree.model.sav\", \"wb\"))"
]
}
],
"metadata": {
"kernelspec": {
"display_name": ".venv",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.9"
}
},
"nbformat": 4,
"nbformat_minor": 2
}

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