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#3 First try, change LinearSVC to SVC

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make a builder
start changing tests
This commit is contained in:
Ricardo Montañana Gómez
2020-06-07 20:26:59 +02:00
parent b824229121
commit 05b462716e
10 changed files with 112 additions and 70 deletions
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4
notebooks/adaboost.ipynb
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@@ -9,7 +9,7 @@
"import time\n",
"from sklearn.ensemble import AdaBoostClassifier\n",
"from sklearn.tree import DecisionTreeClassifier\n",
"from sklearn.svm import LinearSVC\n",
"from sklearn.svm import SVC\n",
"from sklearn.model_selection import GridSearchCV, train_test_split\n",
"from sklearn.datasets import load_iris\n",
"from stree import Stree"
@@ -131,7 +131,7 @@
],
"source": [
"now = time.time()\n",
"clf3 = AdaBoostClassifier(LinearSVC(random_state=random_state), n_estimators=100, random_state=random_state, algorithm='SAMME')\n",
"clf3 = AdaBoostClassifier(SVC(kernel="linear",random_state=random_state), n_estimators=100, random_state=random_state, algorithm='SAMME')\n",
"clf3.fit(Xtrain, ytrain)\n",
"print(\"Score Train: \", clf3.score(Xtrain, ytrain))\n",
"print(\"Score Test: \", clf3.score(Xtest, ytest))\n",

2
notebooks/crcard_graphs.ipynb
View File

@@ -20,7 +20,7 @@
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"from sklearn.svm import LinearSVC\n",
"from sklearn.svm import SVC\n",
"from sklearn.tree import DecisionTreeClassifier\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.datasets import make_classification, load_iris, load_wine\n",

4
notebooks/gridsearch.ipynb
View File

@@ -8,7 +8,7 @@
"source": [
"from sklearn.ensemble import AdaBoostClassifier\n",
"from sklearn.tree import DecisionTreeClassifier\n",
"from sklearn.svm import LinearSVC\n",
"from sklearn.svm import SVC\n",
"from sklearn.model_selection import GridSearchCV, train_test_split\n",
"from sklearn.datasets import load_iris\n",
"from stree import Stree"
@@ -109,7 +109,7 @@
}
],
"source": [
"LinearSVC().get_params(), DecisionTreeClassifier().get_params()"
"SVC(kernel="linear",).get_params(), DecisionTreeClassifier().get_params()"
]
},
{

69
notebooks/test2.ipynb
View File

@@ -14,13 +14,13 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"from sklearn.svm import LinearSVC\n",
"from sklearn.svm import SVC\n",
"from sklearn.tree import DecisionTreeClassifier\n",
"from sklearn.datasets import make_classification, load_iris, load_wine\n",
"from sklearn.model_selection import train_test_split\n",
@@ -42,7 +42,7 @@
},
{
"cell_type": "code",
"execution_count": 19,
"execution_count": 2,
"metadata": {},
"outputs": [
{
@@ -96,13 +96,13 @@
},
{
"cell_type": "code",
"execution_count": 21,
"execution_count": 3,
"metadata": {},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": "Accuracy of Train without weights 0.996415770609319\nAccuracy of Train with weights 0.994026284348865\nAccuracy of Tests without weights 0.9665738161559888\nAccuracy of Tests with weights 0.9721448467966574\n"
"text": "Accuracy of Train without weights 1.0\nAccuracy of Train with weights 1.0\nAccuracy of Tests without weights 0.9554317548746518\nAccuracy of Tests with weights 0.9777158774373259\n"
}
],
"source": [
@@ -115,13 +115,19 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 4,
"metadata": {
"tags": [
"outputPrepend"
]
},
"outputs": [],
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": "************** C=0.001 ****************************\nClassifier's accuracy (train): 0.9749\nClassifier's accuracy (test) : 0.9749\nroot\nroot - Down, <pure> - Leaf class=1 belief= 1.000000 counts=(array([1]), array([117]))\nroot - Up, <cgaf> - Leaf class=0 belief= 0.970833 counts=(array([0, 1]), array([699, 21]))\n\n**************************************************\n************** C=0.01 ****************************\nClassifier's accuracy (train): 0.9797\nClassifier's accuracy (test) : 0.9777\nroot\nroot - Down, <pure> - Leaf class=1 belief= 1.000000 counts=(array([1]), array([121]))\nroot - Up, <cgaf> - Leaf class=0 belief= 0.976257 counts=(array([0, 1]), array([699, 17]))\n\n**************************************************\n************** C=1 ****************************\nClassifier's accuracy (train): 0.9869\nClassifier's accuracy (test) : 0.9805\nroot\nroot - Down, <pure> - Leaf class=1 belief= 1.000000 counts=(array([1]), array([127]))\nroot - Up, <cgaf> - Leaf class=0 belief= 0.984507 counts=(array([0, 1]), array([699, 11]))\n\n**************************************************\n************** C=5 ****************************\nClassifier's accuracy (train): 0.9892\nClassifier's accuracy (test) : 0.9721\nroot\nroot - Down\nroot - Down - Down, <pure> - Leaf class=1 belief= 1.000000 counts=(array([1]), array([128]))\nroot - Down - Up, <pure> - Leaf class=0 belief= 1.000000 counts=(array([0]), array([3]))\nroot - Up\nroot - Up - Down\nroot - Up - Down - Down, <pure> - Leaf class=1 belief= 1.000000 counts=(array([1]), array([1]))\nroot - Up - Down - Up, <pure> - Leaf class=0 belief= 1.000000 counts=(array([0]), array([1]))\nroot - Up - Up, <cgaf> - Leaf class=0 belief= 0.987216 counts=(array([0, 1]), array([695, 9]))\n\n**************************************************\n************** C=17 ****************************\nClassifier's accuracy (train): 0.9952\nClassifier's accuracy (test) : 0.9749\nroot\nroot - Down\nroot - Down - Down, <pure> - Leaf class=1 belief= 1.000000 counts=(array([1]), array([120]))\nroot - Down - Up, <pure> - Leaf class=0 belief= 1.000000 counts=(array([0]), array([57]))\nroot - Up\nroot - Up - Down\nroot - Up - Down - Down, <pure> - Leaf class=1 belief= 1.000000 counts=(array([1]), array([14]))\nroot - Up - Down - Up, <pure> - Leaf class=0 belief= 1.000000 counts=(array([0]), array([12]))\nroot - Up - Up, <cgaf> - Leaf class=0 belief= 0.993691 counts=(array([0, 1]), array([630, 4]))\n\n**************************************************\n0.1084 secs\n"
}
],
"source": [
"t = time.time()\n",
"for C in (.001, .01, 1, 5, 17):\n",
@@ -137,9 +143,15 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 5,
"metadata": {},
"outputs": [],
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": "[[0.95399748 0.04600252]\n [0.92625258 0.07374742]\n [0.97804877 0.02195123]\n [0.94803313 0.05196687]]\n"
}
],
"source": [
"import numpy as np\n",
"from sklearn.preprocessing import StandardScaler\n",
@@ -154,9 +166,15 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 6,
"metadata": {},
"outputs": [],
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": "root\nroot - Down\nroot - Down - Down, <pure> - Leaf class=1 belief= 1.000000 counts=(array([1]), array([120]))\nroot - Down - Up, <pure> - Leaf class=0 belief= 1.000000 counts=(array([0]), array([57]))\nroot - Up\nroot - Up - Down\nroot - Up - Down - Down, <pure> - Leaf class=1 belief= 1.000000 counts=(array([1]), array([14]))\nroot - Up - Down - Up, <pure> - Leaf class=0 belief= 1.000000 counts=(array([0]), array([12]))\nroot - Up - Up, <cgaf> - Leaf class=0 belief= 0.993691 counts=(array([0, 1]), array([630, 4]))\n"
}
],
"source": [
"#check iterator\n",
"for i in list(clf):\n",
@@ -165,9 +183,15 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 7,
"metadata": {},
"outputs": [],
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": "root\nroot - Down\nroot - Down - Down, <pure> - Leaf class=1 belief= 1.000000 counts=(array([1]), array([120]))\nroot - Down - Up, <pure> - Leaf class=0 belief= 1.000000 counts=(array([0]), array([57]))\nroot - Up\nroot - Up - Down\nroot - Up - Down - Down, <pure> - Leaf class=1 belief= 1.000000 counts=(array([1]), array([14]))\nroot - Up - Down - Up, <pure> - Leaf class=0 belief= 1.000000 counts=(array([0]), array([12]))\nroot - Up - Up, <cgaf> - Leaf class=0 belief= 0.993691 counts=(array([0, 1]), array([630, 4]))\n"
}
],
"source": [
"#check iterator again\n",
"for i in clf:\n",
@@ -176,7 +200,7 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
@@ -187,9 +211,15 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 9,
"metadata": {},
"outputs": [],
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": "1 functools.partial(<function check_no_attributes_set_in_init at 0x128922b90>, 'Stree')\n2 functools.partial(<function check_estimators_dtypes at 0x128918cb0>, 'Stree')\n3 functools.partial(<function check_fit_score_takes_y at 0x128918b90>, 'Stree')\n4 functools.partial(<function check_sample_weights_pandas_series at 0x1289144d0>, 'Stree')\n5 functools.partial(<function check_sample_weights_not_an_array at 0x1289145f0>, 'Stree')\n6 functools.partial(<function check_sample_weights_list at 0x128914710>, 'Stree')\n7 functools.partial(<function check_sample_weights_shape at 0x128914830>, 'Stree')\n8 functools.partial(<function check_sample_weights_invariance at 0x128914950>, 'Stree')\n9 functools.partial(<function check_estimators_fit_returns_self at 0x12891ecb0>, 'Stree')\n10 functools.partial(<function check_estimators_fit_returns_self at 0x12891ecb0>, 'Stree', readonly_memmap=True)\n11 functools.partial(<function check_complex_data at 0x128914b00>, 'Stree')\n12 functools.partial(<function check_dtype_object at 0x128914a70>, 'Stree')\n13 functools.partial(<function check_estimators_empty_data_messages at 0x128918dd0>, 'Stree')\n14 functools.partial(<function check_pipeline_consistency at 0x128918a70>, 'Stree')\n15 functools.partial(<function check_estimators_nan_inf at 0x128918ef0>, 'Stree')\n16 functools.partial(<function check_estimators_overwrite_params at 0x128922a70>, 'Stree')\n17 functools.partial(<function check_estimator_sparse_data at 0x1289143b0>, 'Stree')\n18 functools.partial(<function check_estimators_pickle at 0x12891e170>, 'Stree')\n19 functools.partial(<function check_classifier_data_not_an_array at 0x128922dd0>, 'Stree')\n20 functools.partial(<function check_classifiers_one_label at 0x12891e830>, 'Stree')\n21 functools.partial(<function check_classifiers_classes at 0x128922290>, 'Stree')\n22 functools.partial(<function check_estimators_partial_fit_n_features at 0x12891e290>, 'Stree')\n23 functools.partial(<function check_classifiers_train at 0x12891e950>, 'Stree')\n24 functools.partial(<function check_classifiers_train at 0x12891e950>, 'Stree', readonly_memmap=True)\n25 functools.partial(<function check_classifiers_train at 0x12891e950>, 'Stree', readonly_memmap=True, X_dtype='float32')\n26 functools.partial(<function check_classifiers_regression_target at 0x1289278c0>, 'Stree')\n27 functools.partial(<function check_supervised_y_no_nan at 0x12890c4d0>, 'Stree')\n28 functools.partial(<function check_supervised_y_2d at 0x12891eef0>, 'Stree')\n29 functools.partial(<function check_estimators_unfitted at 0x12891edd0>, 'Stree')\n30 functools.partial(<function check_non_transformer_estimators_n_iter at 0x128927440>, 'Stree')\n31 functools.partial(<function check_decision_proba_consistency at 0x1289279e0>, 'Stree')\n32 functools.partial(<function check_fit2d_predict1d at 0x128918050>, 'Stree')\n33 functools.partial(<function check_methods_subset_invariance at 0x128918200>, 'Stree')\n34 functools.partial(<function check_fit2d_1sample at 0x128918320>, 'Stree')\n35 functools.partial(<function check_fit2d_1feature at 0x128918440>, 'Stree')\n36 functools.partial(<function check_fit1d at 0x128918560>, 'Stree')\n37 functools.partial(<function check_get_params_invariance at 0x128927680>, 'Stree')\n38 functools.partial(<function check_set_params at 0x1289277a0>, 'Stree')\n39 functools.partial(<function check_dict_unchanged at 0x128914c20>, 'Stree')\n40 functools.partial(<function check_dont_overwrite_parameters at 0x128914ef0>, 'Stree')\n41 functools.partial(<function check_fit_idempotent at 0x128927b90>, 'Stree')\n42 functools.partial(<function check_n_features_in at 0x128927c20>, 'Stree')\n43 functools.partial(<function check_requires_y_none at 0x128927cb0>, 'Stree')\n"
}
],
"source": [
"# Make checks one by one\n",
"c = 0\n",
@@ -199,6 +229,13 @@
" print(c, check[1])\n",
" check[1](check[0])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {

2
notebooks/test_graphs.ipynb
View File

@@ -34,7 +34,7 @@
"import random\n",
"import numpy as np\n",
"from sklearn.datasets import make_blobs\n",
"from sklearn.svm import LinearSVC\n",
"from sklearn.svm import SVC\n",
"from stree import Stree, Stree_grapher"
]
},