{ "cells": [ { "cell_type": "markdown", "id": "9668cb88", "metadata": {}, "source": [ "# Visualisation of Explanations (Without GUI)" ] }, { "cell_type": "markdown", "id": "71e34ba4", "metadata": {}, "source": [ "You can also display some explanations without the PyXAI's Graphical Interface. To achieve this, the ```Explainer``` object of PyXAI defines several methods. As a reminder, see the [Explainer](/documentation/explainer/concepts/) page for instructions on how to create this object." ] }, { "cell_type": "markdown", "id": "6e9f6784", "metadata": {}, "source": [ "- To display an explanation in a Jupyter notebook: " ] }, { "cell_type": "markdown", "id": "02c30d4b", "metadata": {}, "source": [ "| Explainer.visualisation.notebook(instance, reason, image=None, time_series=None, contrastive=False, width=250):|\n", "|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| \n", "| Use the ```IPython.display()``` method to display images in a Jupyter notebook. Displays for an instance given in the instance parameter an explanation given in the reason parameter. The given instance must be without the label. The given reason must be in Boolean variable form (without using the ```to_features()``` method). The explanation can be shown differently for images and time series, using the parameters provided. |\n", "| instance ```Numpy Array``` of ```Float```: The instance to be explained.|\n", "| reason ```List``` ```Tuple```: A set of (signed) binary variables. Reason (explanation) to display. |\n", "| image ```Dict``` ```None```: Python dictionary containing some information specific to images with 4 keys: [\"shape\", \"dtype\", \"get_pixel_value\", \"instance_index_to_pixel_position\"].| \n", "|\"shape\": Tuple representing the number of horizontal and vertical pixels. If the number of values representing a pixel is not equal to 1, the last value of this tuple have to contain this number (example: (8,8,3) represents an image of 8 * 8 = 64 pixels where each pixel contains 3 values (RGB)). |\n", "|\"dtype\": Domain of values for each pixel (a numpy dtype or can be a tuple of numpy dtype (for a RGB pixel for example)). |\n", "|\"get_pixel_value\": Python function with 4 parameters which returns the value of a pixel according to a pixel position (x,y).|\n", "|\"instance_index_to_pixel_position\": Python function with 2 parameters which returns a pixel position (x,y) according to an index of the instance. |\n", "| time_series ```Dict``` ```None```: To display time series. Python dictionary where a key is the name of a time serie and each value of a key is a list containing time series feature names.| \n", "| contrastive ```Boolean```: ```True``` or ```False``` depending on whether you want to get a contrastive explanation or not. When this parameter is set to ```True```, the elimination of redundant features must be reversed. Default value is ```False```.|\n", "| width ```Integer```: The width parameter specifies width in pixels of the resulting image. Default value is ```250``` pixels. The width value causes the image height to be scaled in proportion to the requested width (preserves the ratio).|" ] }, { "cell_type": "markdown", "id": "fa05b32a", "metadata": {}, "source": [ "- To display an explanation on screen: " ] }, { "cell_type": "markdown", "id": "17832903", "metadata": {}, "source": [ "| Explainer.visualisation.screen(instance, reason, image=None, time_series=None, contrastive=False, width=250):|\n", "|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| \n", "| Use the ```Image.show()``` method to display images on screen with the [PIL library](https://pillow.readthedocs.io/en/latest/reference/Image.html#PIL.Image.Image.show). Displays for an instance given in the instance parameter an explanation given in the reason parameter. The given instance must be without the label. The given reason must be in Boolean variable form (without using the ```to_features()``` method). The explanation can be shown differently for images and time series, using the parameters provided. |\n", "| instance ```Numpy Array``` of ```Float```: The instance to be explained.|\n", "| reason ```List``` ```Tuple```: A set of (signed) binary variables. Reason (explanation) to display. |\n", "| image ```Dict``` ```None```: Python dictionary containing some information specific to images with 4 keys: [\"shape\", \"dtype\", \"get_pixel_value\", \"instance_index_to_pixel_position\"].| \n", "|\"shape\": Tuple representing the number of horizontal and vertical pixels. If the number of values representing a pixel is not equal to 1, the last value of this tuple have to contain this number (example: (8,8,3) represents an image of 8 * 8 = 64 pixels where each pixel contains 3 values (RGB)). |\n", "|\"dtype\": Domain of values for each pixel (a numpy dtype or can be a tuple of numpy dtype (for a RGB pixel for example)). |\n", "|\"get_pixel_value\": Python function with 4 parameters which returns the value of a pixel according to a pixel position (x,y).|\n", "|\"instance_index_to_pixel_position\": Python function with 2 parameters which returns a pixel position (x,y) according to an index of the instance. |\n", "| time_series ```Dict``` ```None```: To display time series. Python dictionary where a key is the name of a time serie and each value of a key is a list containing time series feature names.| \n", "| contrastive ```Boolean```: ```True``` or ```False``` depending on whether you want to get a contrastive explanation or not. When this parameter is set to ```True```, the elimination of redundant features must be reversed. Default value is ```False```.|\n", "| width ```Integer```: The width parameter specifies width in pixels of the resulting image. Default value is ```250``` pixels. The width value causes the image height to be scaled in proportion to the requested width (preserves the ratio).|" ] }, { "cell_type": "markdown", "id": "68e43d77", "metadata": {}, "source": [ "- To return [PIL images](https://pillow.readthedocs.io/en/latest/reference/Image.html) of an explanation: " ] }, { "cell_type": "markdown", "id": "fb0748ec", "metadata": {}, "source": [ "| Explainer.visualisation.get_PILImage(instance, reason, image=None, time_series=None, contrastive=False, width=250):|\n", "|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| \n", "| Return PIL images of the [PIL library](https://pillow.readthedocs.io/en/latest/reference/Image.html) for an instance given in the instance parameter an explanation given in the reason parameter. The given instance must be without the label. The given reason must be in Boolean variable form (without using the ```to_features()``` method). The explanation can be shown differently for images and time series, using the parameters provided. |\n", "| instance ```Numpy Array``` of ```Float```: The instance to be explained.|\n", "| reason ```List``` ```Tuple```: A set of (signed) binary variables. Reason (explanation) to display. |\n", "| image ```Dict``` ```None```: Python dictionary containing some information specific to images with 4 keys: [\"shape\", \"dtype\", \"get_pixel_value\", \"instance_index_to_pixel_position\"].| \n", "|\"shape\": Tuple representing the number of horizontal and vertical pixels. If the number of values representing a pixel is not equal to 1, the last value of this tuple have to contain this number (example: (8,8,3) represents an image of 8 * 8 = 64 pixels where each pixel contains 3 values (RGB)). |\n", "|\"dtype\": Domain of values for each pixel (a numpy dtype or can be a tuple of numpy dtype (for a RGB pixel for example)). |\n", "|\"get_pixel_value\": Python function with 4 parameters which returns the value of a pixel according to a pixel position (x,y).|\n", "|\"instance_index_to_pixel_position\": Python function with 2 parameters which returns a pixel position (x,y) according to an index of the instance. |\n", "| time_series ```Dict``` ```None```: To display time series. Python dictionary where a key is the name of a time serie and each value of a key is a list containing time series feature names.| \n", "| contrastive ```Boolean```: ```True``` or ```False``` depending on whether you want to get a contrastive explanation or not. When this parameter is set to ```True```, the elimination of redundant features must be reversed. Default value is ```False```.|\n", "| width ```Integer```: The width parameter specifies width in pixels of the resulting image. Default value is ```250``` pixels. The width value causes the image height to be scaled in proportion to the requested width (preserves the ratio).|" ] }, { "cell_type": "markdown", "id": "307ee4a9", "metadata": {}, "source": [ "- To save PNG image files in the current directory with the [PIL library](https://pillow.readthedocs.io/en/latest/reference/Image.html):" ] }, { "cell_type": "markdown", "id": "37134f8b", "metadata": {}, "source": [ "| Explainer.visualisation.save_png(file, instance, reason, image=None, time_series=None, contrastive=False, width=250):|\n", "|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| \n", "| Save PNG image files in the current directory (named with the file parameter) with the [PIL library](https://pillow.readthedocs.io/en/latest/reference/Image.html) for an instance given in the instance parameter an explanation given in the reason parameter. The given instance must be without the label. The given reason must be in Boolean variable form (without using the ```to_features()``` method). The explanation can be shown differently for images and time series, using the parameters provided. |\n", "| file ```String```: The filename.|\n", "| instance ```Numpy Array``` of ```Float```: The instance to be explained.|\n", "| reason ```List``` ```Tuple```: A set of (signed) binary variables. Reason (explanation) to display. |\n", "| image ```Dict``` ```None```: Python dictionary containing some information specific to images with 4 keys: [\"shape\", \"dtype\", \"get_pixel_value\", \"instance_index_to_pixel_position\"].| \n", "|\"shape\": Tuple representing the number of horizontal and vertical pixels. If the number of values representing a pixel is not equal to 1, the last value of this tuple have to contain this number (example: (8,8,3) represents an image of 8 * 8 = 64 pixels where each pixel contains 3 values (RGB)). |\n", "|\"dtype\": Domain of values for each pixel (a numpy dtype or can be a tuple of numpy dtype (for a RGB pixel for example)). |\n", "|\"get_pixel_value\": Python function with 4 parameters which returns the value of a pixel according to a pixel position (x,y).|\n", "|\"instance_index_to_pixel_position\": Python function with 2 parameters which returns a pixel position (x,y) according to an index of the instance. |\n", "| time_series ```Dict``` ```None```: To display time series. Python dictionary where a key is the name of a time serie and each value of a key is a list containing time series feature names.| \n", "| contrastive ```Boolean```: ```True``` or ```False``` depending on whether you want to get a contrastive explanation or not. When this parameter is set to ```True```, the elimination of redundant features must be reversed. Default value is ```False```.|\n", "| width ```Integer```: The width parameter specifies width in pixels of the resulting image. Default value is ```250``` pixels. The width value causes the image height to be scaled in proportion to the requested width (preserves the ratio).|" ] }, { "cell_type": "markdown", "id": "1b92c607", "metadata": {}, "source": [ "- A PIL image can be resized thank to:" ] }, { "cell_type": "markdown", "id": "fe415eec", "metadata": {}, "source": [ "| Explainer.visualisation.resize_PILimage(image, width=250):|\n", "|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| \n", "| The width parameter specifies width in pixels of the resulting image. The width value causes the image height to be scaled in proportion to the requested width (preserves the ratio). |\n", "| image ```PILimage```: Image from the [PIL library](https://pillow.readthedocs.io/en/latest/reference/Image.html).|\n", "| width ```Integer```: The width parameter specifies width in pixels of the resulting image. Default value is ```250``` pixels. The width value causes the image height to be scaled in proportion to the requested width (preserves the ratio).|" ] }, { "cell_type": "markdown", "id": "d9decb37", "metadata": {}, "source": [ "## With a tabular dataset" ] }, { "cell_type": "markdown", "id": "aad10b33", "metadata": {}, "source": [ "The [Australian Credit Approval dataset](https://www.openml.org/search?type=data&sort=runs&id=40981&status=active) is a credit card application:" ] }, { "cell_type": "code", "execution_count": 1, "id": "8358275a", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "data:\n", " A1 A2 A3 A4_1 A4_2 A4_3 A5_1 A5_2 A5_3 A5_4 ... A8 A9 A10 \n", "0 1 65 168 0 1 0 0 0 0 1 ... 0 0 1 \\\n", "1 0 72 123 0 1 0 0 0 0 0 ... 0 0 1 \n", "2 0 142 52 1 0 0 0 0 0 1 ... 0 0 1 \n", "3 0 60 169 1 0 0 0 0 0 0 ... 1 1 12 \n", "4 1 44 134 0 1 0 0 0 0 0 ... 1 1 15 \n", ".. .. ... ... ... ... ... ... ... ... ... ... .. .. ... \n", "685 1 163 160 0 1 0 0 0 0 0 ... 1 0 1 \n", "686 1 49 14 0 1 0 0 0 0 0 ... 0 0 1 \n", "687 0 32 145 0 1 0 0 0 0 0 ... 1 0 1 \n", "688 0 122 193 0 1 0 0 0 0 0 ... 1 1 2 \n", "689 1 245 2 0 1 0 0 0 0 0 ... 0 1 2 \n", "\n", " A11 A12_1 A12_2 A12_3 A13 A14 A15 \n", "0 1 0 1 0 32 161 0 \n", "1 0 0 1 0 53 1 0 \n", "2 1 0 1 0 98 1 0 \n", "3 1 0 1 0 1 1 1 \n", "4 0 0 1 0 18 68 1 \n", ".. ... ... ... ... ... ... ... \n", "685 0 0 1 0 1 1 1 \n", "686 0 0 1 0 1 35 0 \n", "687 0 0 1 0 32 1 1 \n", "688 0 0 1 0 38 12 1 \n", "689 0 1 0 0 159 1 1 \n", "\n", "[690 rows x 39 columns]\n", "-------------- Information ---------------\n", "Dataset name: ../dataset/australian_0.csv\n", "nFeatures (nAttributes, with the labels): 39\n", "nInstances (nObservations): 690\n", "nLabels: 2\n", "--------------- Evaluation ---------------\n", "method: HoldOut\n", "output: RF\n", "learner_type: Classification\n", "learner_options: {'max_depth': None, 'random_state': 0}\n", "--------- Evaluation Information ---------\n", "For the evaluation number 0:\n", "metrics:\n", " accuracy: 85.5072463768116\n", " precision: 84.70588235294117\n", " recall: 80.89887640449437\n", " f1_score: 82.75862068965516\n", " specificity: 88.98305084745762\n", " true_positive: 72\n", " true_negative: 105\n", " false_positive: 13\n", " false_negative: 17\n", " sklearn_confusion_matrix: [[105, 13], [17, 72]]\n", "nTraining instances: 483\n", "nTest instances: 207\n", "\n", "--------------- Explainer ----------------\n", "For the evaluation number 0:\n", "**Random Forest Model**\n", "nClasses: 2\n", "nTrees: 100\n", "nVariables: 1361\n", "\n", "--------------- Instances ----------------\n", "number of instances selected: 1\n", "----------------------------------------------\n" ] } ], "source": [ "from pyxai import Learning, Explainer\n", "\n", "# Machine learning part\n", "learner = Learning.Scikitlearn(\"../dataset/australian_0.csv\", learner_type=Learning.CLASSIFICATION)\n", "model = learner.evaluate(method=Learning.HOLD_OUT, output=Learning.RF)\n", "instance, prediction = learner.get_instances(model, n=1, seed=11200, correct=True)\n", "\n", "australian_types = {\n", " \"numerical\": Learning.DEFAULT,\n", " \"categorical\": {\"A4*\": (1, 2, 3), \n", " \"A5*\": tuple(range(1, 15)),\n", " \"A6*\": (1, 2, 3, 4, 5, 7, 8, 9), \n", " \"A12*\": tuple(range(1, 4))},\n", " \"binary\": [\"A1\", \"A8\", \"A9\", \"A11\"],\n", "}" ] }, { "cell_type": "code", "execution_count": 2, "id": "695c04b9", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "--------- Theory Feature Types -----------\n", "Before the encoding (without one hot encoded features), we have:\n", "Numerical features: 6\n", "Categorical features: 4\n", "Binary features: 4\n", "Number of features: 14\n", "Values of categorical features: {'A4_1': ['A4', 1, (1, 2, 3)], 'A4_2': ['A4', 2, (1, 2, 3)], 'A4_3': ['A4', 3, (1, 2, 3)], 'A5_1': ['A5', 1, (1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14)], 'A5_2': ['A5', 2, (1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14)], 'A5_3': ['A5', 3, (1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14)], 'A5_4': ['A5', 4, (1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14)], 'A5_5': ['A5', 5, (1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14)], 'A5_6': ['A5', 6, (1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14)], 'A5_7': ['A5', 7, (1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14)], 'A5_8': ['A5', 8, (1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14)], 'A5_9': ['A5', 9, (1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14)], 'A5_10': ['A5', 10, (1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14)], 'A5_11': ['A5', 11, (1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14)], 'A5_12': ['A5', 12, (1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14)], 'A5_13': ['A5', 13, (1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14)], 'A5_14': ['A5', 14, (1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14)], 'A6_1': ['A6', 1, (1, 2, 3, 4, 5, 7, 8, 9)], 'A6_2': ['A6', 2, (1, 2, 3, 4, 5, 7, 8, 9)], 'A6_3': ['A6', 3, (1, 2, 3, 4, 5, 7, 8, 9)], 'A6_4': ['A6', 4, (1, 2, 3, 4, 5, 7, 8, 9)], 'A6_5': ['A6', 5, (1, 2, 3, 4, 5, 7, 8, 9)], 'A6_7': ['A6', 7, (1, 2, 3, 4, 5, 7, 8, 9)], 'A6_8': ['A6', 8, (1, 2, 3, 4, 5, 7, 8, 9)], 'A6_9': ['A6', 9, (1, 2, 3, 4, 5, 7, 8, 9)], 'A12_1': ['A12', 1, (1, 2, 3)], 'A12_2': ['A12', 2, (1, 2, 3)], 'A12_3': ['A12', 3, (1, 2, 3)]}\n", "\n", "Number of used features in the model (before the encoding): 14\n", "Number of used features in the model (after the encoding): 38\n", "----------------------------------------------\n", "majoritary_reason 12\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "explainer = Explainer.initialize(model, instance, features_type=australian_types)\n", "majoritary_reason = explainer.majoritary_reason(time_limit=10)\n", "print(\"majoritary_reason\", len(majoritary_reason))\n", "explainer.visualisation.notebook(instance, majoritary_reason)" ] }, { "cell_type": "markdown", "id": "9b70d98c", "metadata": {}, "source": [ "## With an image dataset" ] }, { "cell_type": "markdown", "id": "efcf8c54", "metadata": {}, "source": [ "We use a modified version of [MNIST](/assets/notebooks/dataset/mnist49.csv) dataset that focuses on 4 and 9 digits. We create a model using the hold-out approach (by default, the test size is set to 30%). We choose to use a Boosted Tree by using XGBoost. " ] }, { "cell_type": "code", "execution_count": 3, "id": "c5f1d289", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "data:\n", " 0 1 2 3 4 5 6 7 8 9 ... 775 776 777 778 779 780 781 \n", "0 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 \\\n", "1 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 \n", "2 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 \n", "3 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 \n", "4 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 \n", "... .. .. .. .. .. .. .. .. .. .. ... ... ... ... ... ... ... ... \n", "13777 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 \n", "13778 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 \n", "13779 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 \n", "13780 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 \n", "13781 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 \n", "\n", " 782 783 784 \n", "0 0 0 4 \n", "1 0 0 9 \n", "2 0 0 4 \n", "3 0 0 9 \n", "4 0 0 4 \n", "... ... ... ... \n", "13777 0 0 4 \n", "13778 0 0 4 \n", "13779 0 0 4 \n", "13780 0 0 9 \n", "13781 0 0 4 \n", "\n", "[13782 rows x 785 columns]\n", "-------------- Information ---------------\n", "Dataset name: ../dataset/mnist49.csv\n", "nFeatures (nAttributes, with the labels): 785\n", "nInstances (nObservations): 13782\n", "nLabels: 2\n", "--------------- Evaluation ---------------\n", "method: HoldOut\n", "output: BT\n", "learner_type: Classification\n", "learner_options: {'seed': 0, 'max_depth': None, 'eval_metric': 'mlogloss'}\n", "--------- Evaluation Information ---------\n", "For the evaluation number 0:\n", "metrics:\n", " accuracy: 98.57315598548972\n", " precision: 98.53911404335533\n", " recall: 98.67862199150542\n", " f1_score: 98.60881867484083\n", " specificity: 98.4623015873016\n", " true_positive: 2091\n", " true_negative: 1985\n", " false_positive: 31\n", " false_negative: 28\n", " sklearn_confusion_matrix: [[1985, 31], [28, 2091]]\n", "nTraining instances: 9647\n", "nTest instances: 4135\n", "\n", "--------------- Explainer ----------------\n", "For the evaluation number 0:\n", "**Boosted Tree model**\n", "NClasses: 2\n", "nTrees: 100\n", "nVariables: 1590\n", "\n", "--------------- Instances ----------------\n", "number of instances selected: 1\n", "----------------------------------------------\n" ] } ], "source": [ "from pyxai import Learning, Explainer, Tools\n", "\n", "\n", "# Machine learning part\n", "learner = Learning.Xgboost(\"../dataset/mnist49.csv\", learner_type=Learning.CLASSIFICATION)\n", "model = learner.evaluate(method=Learning.HOLD_OUT, output=Learning.BT)\n", "instance, prediction = learner.get_instances(model, n=1, correct=True, predictions=[0])" ] }, { "cell_type": "code", "execution_count": 4, "id": "294ee849", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "len minimal tree_specific_reason: 187\n" ] } ], "source": [ "# Explanation part\n", "explainer = Explainer.initialize(model, instance)\n", "minimal_tree_specific_reason = explainer.minimal_tree_specific_reason(time_limit=10)\n", "print(\"len minimal tree_specific_reason:\", len(minimal_tree_specific_reason))" ] }, { "cell_type": "markdown", "id": "18f53e4f", "metadata": {}, "source": [ "For a dataset containing images, you need to give certain information specific to images (through the ```image``` parameter of the ```show``` method) in order to display instances and explanations correctly. Here we give an example for the MNIST images, each of which is composed of 28 $\\times$ 28 pixels, and the value of a pixel is an 8-bit integer:" ] }, { "cell_type": "code", "execution_count": 5, "id": "e0c19fde", "metadata": {}, "outputs": [ { "data": { "image/jpeg": 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\n", 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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import numpy\n", "\n", "def get_pixel_value(instance, x, y, shape):\n", " index = x * shape[0] + y \n", " return instance[index]\n", "\n", "def instance_index_to_pixel_position(i, shape):\n", " return i // shape[0], i % shape[0]\n", "\n", "explainer.visualisation.notebook(instance, minimal_tree_specific_reason, image={\"shape\": (28,28),\n", " \"dtype\": numpy.uint8,\n", " \"get_pixel_value\": get_pixel_value,\n", " \"instance_index_to_pixel_position\": instance_index_to_pixel_position})" ] }, { "cell_type": "markdown", "id": "5a20b7c0", "metadata": {}, "source": [ "Blue (resp. Red) pixels represent positive (resp. negative) conditions in the explanation. A blue (resp. red) pixel means that the color of the pixel must be white (black) to be included in the explanation. In this way, all instances (images) that have white pixels when the explanation has blue pixels and have black pixels when the explanation has red pixels will have the same classification (in our case, it will be 4)." ] }, { "cell_type": "markdown", "id": "f6899e8e", "metadata": {}, "source": [ "You can open on your screen the images with:" ] }, { "cell_type": "code", "execution_count": 6, "id": "416bb5a0", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "explainer.visualisation.screen(instance, minimal_tree_specific_reason, image={\"shape\": (28,28),\n", " \"dtype\": numpy.uint8,\n", " \"get_pixel_value\": get_pixel_value,\n", " \"instance_index_to_pixel_position\": instance_index_to_pixel_position})" ] }, { "cell_type": "markdown", "id": "ecc78662", "metadata": {}, "source": [ "You can save PNG files with:" ] }, { "cell_type": "code", "execution_count": 7, "id": "97162936", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "explainer.visualisation.save_png(\"mnist49_example.png\", instance, minimal_tree_specific_reason, image={\"shape\": (28,28),\n", " \"dtype\": numpy.uint8,\n", " \"get_pixel_value\": get_pixel_value,\n", " \"instance_index_to_pixel_position\": instance_index_to_pixel_position})" ] }, { "cell_type": "markdown", "id": "27e81074", "metadata": {}, "source": [ "You can get PIL images with:" ] }, { "cell_type": "code", "execution_count": 8, "id": "b17620a1", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[, ]\n" ] }, { "data": { "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "images = explainer.visualisation.get_PILImage(instance, minimal_tree_specific_reason, image={\"shape\": (28,28),\n", " \"dtype\": numpy.uint8,\n", " \"get_pixel_value\": get_pixel_value,\n", " \"instance_index_to_pixel_position\": instance_index_to_pixel_position})\n", "print(images)" ] }, { "cell_type": "markdown", "id": "5d66c6e1", "metadata": {}, "source": [ "You can resize a PIL image with:" ] }, { "cell_type": "code", "execution_count": 9, "id": "4206d352", "metadata": {}, "outputs": [ { "data": { "image/png": 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C41Z8OhLVk1uAeE2iJSA+fNdKS5iDNOtpyiSvMWeHF4QIDIApUDpQHecBUcntdAFATMBwwCuiLOtGu6HyOmfaNP1CWOyzTs6if8RzRfJuQgh6/f5D9J0d8ZuYsTuR1Ib3hvsAMk7NT60AQE3T9MTai1KOtIep41xBUKsn9vfhnMzxTVryfAft6ALqCjAMUqZomymTCSZXITIwkOYMhqDwXfWxcBCH5yyfAbLVuIjTo0zSXwhE+fjdCMsieX5CijA3XcDok6hrDDJQSq6RSBMm6hesVy8OWMbllEv2GQLs0qwLUZoky9AtPdRTi8p+GRlmWt7ifEbh0ezI6CFRVf8ArRJAnlMAljBarTPya0L/YnyY5mj8HECumLh+ZCXyw/lVOidKXLCWbmQVYT0079IdnT7znwcBkUmIRkiHgwyThcSY8TSPpTQVbcrqTLwvsyrCBUiHabg5PD1NDZhoaKDNlolxm2tKPpAHUAPqaZJCqqmr/9Bvecya4xxlm2SlVf8zN/trugUNaZPm2GfGf/zj20hextIvtOl/bOd2iaDQ7grNdROEC2w3gPPLr+fpnz/9mv7x04d0hakl00JEhT/k2Q79tqo2gvjf6M/26OikMFkwCiD27aDVioiwHIzSiK0VOJ2hs1nTJRG5UNc8l+DoUF0AagGEpkRmZYZrokyVnKYuUVA4dHyEkziLdNi/IS+szJrFvRzdRJX4Y4S1MLWC9mpWDQBMyRy8fpWOfngT5661R6kELPyFERZ9zOf4Q1L+H6fz9NPP79L//Ptd+ufPH8m/jdLRQS+dEZzETB2N6xTt1jCExOVmn/PvZ4Dc17jM0eQUTREMNUYGy8C2Ttaoh4+gOH8x2pERao8ZWjXD54PJXLNoeprsLuzCqLopC/BhsvX1A2qU7eionecIXilekzfS0XWOwVEf1hfoo+OY95S6EYJDm9uKGtC8pt2JWsK6zserUfqVAOMDqRqXaxsR3aEZ+kRNcDW2oIM2woI8s/+QzkcBUgaESIXkyig/RZskuu+2TSZyPcNIGK30xexcpvudouzHcz5bfUx1g3bWQLWQsFSf4Wxfc2I9i4lFbf+Y9ZLNghZM1ZwMAJyceNrn6MSzfXiU9oYHhNz4DQIL1AcGk3WG8Ss0iWg8rRGWJTO7Kd8n5LEu8RkjwmFNZBt/YgR2iOYeEVofkowUlLxxI5s9hc4xPnd5NCD1rgUiGKrJQnJ1xibfZEyfYxsA1B6ZjyohwUglJsI1CjUtNI7rMmcNM5wTyHCBMJnnxHKGQ9dsCFAM/BapZfSm4yMXJpPILRlxHWASh0RKDfqbr1h3abYS+1jS6IIsAUxuTG8IgdG4Gese9LWA7gVMn+Nv3AR3w7Ub6q0BbthlPCfmqvrpxzev0huAPiIcN5TXn5hGYTgxBo9qShHM5wDn0YA4Kwk1rXqFrUGYX1XjFgxXM2KTHHZfk6YfCVA8NgEP6Y1SDSIcKiZigja4U1EfEIAJHIw1TVPAXwBcLLUKGh9T5bEuwvykAfPc3W5pk6a5upmlD2zZuWYNfMEcZ298mToLIGrg96BhgVmb8fwUeZkgCDeEuS5Ctdg1cryP2TtqkW4/TD++fZPeAMqxgBBsZECqGfrd8Lf4EkR8x59HA1L6EBQjGzUjRBaJj0UkpH1FSLnuEaL2iIZMLYQ/ySYnAI3oSjnHfPHclEFNSK9oLpwEolYMOicDPXaaThBtP2uPAM1gXpNgoEt/Zgzaht8QEttaEYLzi1F6d36dfvlAGp35xpxsc3N0kbrLWRr08DMGEGRr1wBpFsF9Vu7f1by2eROrByDuaD9B696+PUtnpOkPMVs9NESBcxwKyW9VngRI3gHCpAtJiwkXTFXKR+zG6EJrkwG0nVFz/6FiesTNAjN2AV5j5qfrZupxTbN3wOJPj2hMZ675csPDhLnGDNMiL9YIhEk9zc0Kv3CNIFwA6A3ziA8fcdAXN3mHiJvpWBNf31ym9moeUdPpD8t0TDzbhTZFpQQrMtmdMd1ui1R7j/WOIaaKtXPmMkN8o35SUXIF1KBD8xvfHxrgE69/AyB3UrGZxGHPncC5/n3N7d7CdAWREf8daG/Z8PalcovETdGCUYuNC2hVb59IjdW4ExaABuwoVOojO4A5WxEZrU1WYv4agAV+sUnPvn9i/vCPf/2Sfn73IX1kUjcGOIE0NeOkcY4v2mPr6+liPy0BvIOZaptMBPAWjDV3peaHLjtMzjXDZgN05IJRJVUCiNCQ0BIgyQqfh8lz31seDUjY8qKq0gHBDsIQ2NDxEindI+WO3pB3ImYn6upzfLjc4kzZhYKvmOCEE+Ykscmg5S53zEqjg3ZgltgTksb0O6W/BVGVvktwdMgrGH3J3q6f/v1L+q//+p/0j3/+gsm6CjOXk4S0gdDModHvA7btLGFyAx9nvkwTZfg7QzAmtD+DlrFLBQDfI6g45ugbU5ozswxRKh6EP60DUD9/eNBfvfNNgMSOdzRCR+1HaXUxxyjplliyiXS5/bNHvqhr1IIf0AcsYIiS7sDWrgBSdNAfsfcXpDBmK0NNQlQc/5TzyzFbi66X7PniJRn8i9tV7UumdjtsYkM75jxvxuASAG7oT7O24lk8DppFBzDIcLvLzvnePm86DXtExifplAnjMSkY18hdknLSaX4OQ4uDBzz8yWhK+L1upX6H19a6gzQESEP6CN8BxOBEDYo5CW3E/2izkeX3lkcBkp2Y4WoGwuxu7PqD0XmCiLIDSJv7oyWby6aj1Lr6FOdGMdc309iKOR5jQnjWgnAGUALqSk+LHRyzMU6ZXernH27TFT7h3a+f2GxwBZOWTPo66eR4PzYXDHtMOBm80jtma2eLxl5j5pwwXiPV45lZBDIIaILvdOwTEr86O0hvWA8/Y/n29clhOsDs+Y6JPrAFoCvau4a+i+sxEAEStBBvpUNM6gHgtqC7g7nDhgUwgmEJT6KlcnKDWSzzpnzTG99WPgMkVPHeNrKdDcmAQLuKD4TdakqIqpY4xBlqck0IuT7/mNY4+oubSSTr3n+8xrywlxfmmqzroT196g96fPqYj1Ub7WAd+/KWPVHTiJJ+eneePn4i/Y5UHrHtZv7jGa8NH6dbpF2brrm0HCPtZ+yVWsCUG/bdnl9j5jgq9YeYzrevjtPffjyNDQpdGNoHJPX05poXfaDVMS0BZowJ/XRleO0caZ1OMFOXhNMjnPwQ89vCVBqMRIRpx1vl+7XD5j4DZKsPvqgdglTyVa4l9wkZZxN3EOaNy+sOjtzcEPdmoHSJbb+awnxmyZqld4SgvxKKXo1wtpg8E3XDXoeNZ510xGc959UFPiu0RBOtlF9dsm6Bf7jBKZseaSOQ0yE74dEOTVaH1wGcF7iceoAG7LOJrQENN6N5+vWCHYfMRZiOxqaEv//H6/R///omHbLbpBQ3VLss4LxJQEKW6TzvN8NPkYB0XuQWJCetht5F+j2aKFUjfDeFEydj0Y6m8nvKg4CUSMKjBJujMn1ggjEIQsVNW8wMPXGwsVOQaGUOsSPUfoqTv+L4gRD0A1Ku2VryTJeQs40DN5NqFnc0NlWC85x2kWbzYexIB/CzU3NY5JEOkFgc7YA3sobQ0UF6jXmG0NI3vc78YOi8xPaoJ3PwVjwLj6D5FW2+Yd5xiKmqF/2B0ZXFg+mSAWAfUV/zc8payRFblkzhm6xsGvYyXrXDzRVuQ/VjfswEqLm6DoJhTq6UAFAJ+waQtgCpmyuBKKAIiJpRNEVwfDmyB/NcPVziTAXKSZYLOyNs8aeYC4zTOUm7GzRDidrH1JwdD9MrzE8XkxUOVG0Y46BXRmfDdDJgmw3MayL58yWRGO3NaGON1nXoZwiSR10nbmfpkGyur6A16XvJfGSECVqwOtm4niZfT+mgVvsECh35vsOYW9IkBgKugvoGmHPWQ+hTe7td6CQx+fc3b9Lrv/yQDknj6z+aZgXgxQKtdXuT+TY1yI0T7vNyKbjswhSUrEm57zpvvfdQ2QKkXikacxAUAfFTzFYAgn0VJG3w0sGh/hPWI25X7DhsskXnlmjIGKaJzR7oL5DW04P0H2+ZAeNgW3DgnNTGz+9Jm5DmWFNvr8Nk7ORV+tvffoDhx0gbE0dMghu0b1g5nPGueAMzOSQ8PmXyeXzC+gk2fs4S7uU5u/BvmHOYqQXEmGSSCukRMDRYjnV70h4MtSyZtV+QpIzdib5OTYhroKJfG+IvjkhOvn79Ov3lhx/T6x8AhHPnLBHqwwe1wheI3G8mIL0FAoZgyLPPGL8jCEHAF/48CMjuM7GGgepFyhyiXJcQoNmsH4C4ujbuTQOAxRow1mgRYeP+PmYECVX63r46TH99e5pen7KhmWv7+BXEDqm65rlWOmVH49kZCT0k84ykXs4Pw0BMxyVSeG1kBPNaMLaHQLQwgU0AQpWcckcbTuRa0NbGjA1Ig/SJzhQmQ28ZOcMXfCJ/9uv7d+k9u/A/fPyQLvFXpnBkaAvTpW8aYH40V0P6VvLrpVgOrwlAAKUWPkN5NCCbviBADdFcmdPqQWwk/VD7AdLS7e+T9znEZrMujVOXEWZIhzDzFFP1ip1/xyz8qHvWbXeNkFiCxSG7K/AVm+P2sdkFDPtt9Yfp2E1zMNYkpFGQ+bMRmxV05Ibexn3uvXK2j1OLtE0PR9+DqW6uVqBc7XSJ9p//epf+/a9/p/f8MkO8aqCWQ79t9IkY2gPSK0RZK9I60rVbbMuVzSH9mEZxIuwmDMHxu2WjKUreN5SvAlIa3qgjHSghxviNLgQATpmfyJj9gyXmhrULpNY3V50Y2kaHSV0Pp2nI6eTO0u2xsnd4zJoEEk4d77nl/76NbHs48QGAdGCqv9rgqwjzj0RgzH20276ycHTyhjkCZhL6gkbO2zwXoSraPOZZteHndx/Tf//rffoFDRmNmMAChiXWVhZkrftzZulsiICuSP/H3eqPWqT2kQGO7bD0ZZ9qpJpiRAiuOcjhkcK/ehNfOv8iIKUxBxdqWmtJ4J051yW5dvtRp6wBpeMHapbVPW8bEflpozl7/ZydXeEDxm4RwqEbbQ3apywVH0emwGcMMpoyayfqUUtGmD1XBd3IcM3yrdGeYOjMcWX4P7Yw4QNXLgGgjdgyOIu2UORD3sCnKUSnZMR9hXpPKV8EpN5gAcSjRPjJBGVCg+AHaKu385hzmWxy0A0Liptr8xFy8i0Sm5gH/Yi2fa1Ec/RjtFOK/m23yLs2zlnnbZTn9xmhsquG7t3tmCbBFJ0B7AlzFkNw+zYrYV7IMTt+aTDUjcWq3U4wxqZQ6kHRZ1W+cOFzqr9Q2VsFDI8yK0qc59Pn+qt/MmtsH/5XL5oHna3OWpvtHMCtR5mmes3aOYx014hznbdnh5iuo3RB1HbhssEY04pAG2H95Yez9P/+z4/pb3/9MZ2SqukygTVTgHREhsEWt6mp9RGnUAvt6BLmq9ISn38kjx4NyH2DVVqKxhSyol5QzJ9dyqWvesb6uW6tnvfkDEVmlzUH24mNeNWglNB40R9ptj2/a0YMXbXjXnPG7Xvx8Z32pFMTe8Ray49vjnkzipc5AeSc91b84YMejD9l/ePvf3md/hNAfviRTXloWYu5zHqh2YK2yhhInxvyzJc5htKH10uh+l15JBg+8GhA7lrPZwWMz697JVOzRRRXC10FXNu4q53xK/dioOUBpBMrHkzxIAOcfzSGFYdoJzLQRjiYu0LbLWYFrDLw1HHXoYHD0QHJRtbNjfiM/Mw29NGyM669JUn59hUv9BBclEK8RZtmc2vMpz3N0maBi77LeIz8slmDvjKGaqylzYeO3wVIvdFCjNeCIXGSa8j2O2Xx7E58MvNyvUx8ttXVlThY28HTcHwXkC2pZNBhJkKQs5bxt6rv2V3vNqD2Kf0D5ijHh4O0YDXRtNAZJuqAuUv+1Z/oqvpDGzZHM/GKhlrIuUsOt4Tf7it2ocy9YQqSvsxXKdyiWgTMhoJHNlRAqndRnT8ZkN2O6t/LebAv/uTe7qPDuqVKYVt5vtAb12v1AkQkcrNpwopVeA0ycFx9yiBFW3ZMJ2qRb0hNzUVxNFtwCACNBr/mQE5MzXFdZklisbbzK7fFjD9Qoa0QCOmBuWuOrkz6M1G+JSazfW3PLEMAIngWtakuVNS7rzwZkHpjuwws9zIjy7eHj/eTVqsP8fYhT4KznMfgHGAZ2EZ7ZD5M4PsGDDuIZ/LvlkyJrFxj8VG1xOjdmfkAPyKTp4TGvT4rh2QDYiNeEBh/7oji4TJuo0Jfq4iXWrnuRNFrllInkq93Tz949iyAPNj6c92oBn8nmdjzmHRq13Hk9lNj0EPdiplzC0NqjzLLZCKKQihMKoaFLoH2FxycrzDVy21H8zVAaEjgCrOly6DCCaNPxDv6RTMeIuaB6y8XELlXFQeukyzFPFqDnJMbpslvlMvfdqz4a9uFsYLhRDAD5u5+ViZr96ODCgxNpCrrs/oMo754ZQ9A8g+tZaMXvo8HBTAK9b9UXi4gO1TXJbLcUhqLKfjyMPMT8qIAIKMtRkmmeGKXimDgm5zbmPKJENy1ZkoBTbYGc1mc4mJcdx4UofjyiGvkLkx8oi2WDSCclzbixgN/Xi4gNUlyUEquoSd/YihhvjALjxlkfewZlHzFpooJW+xVmkFfMjxLdO6r/rzn+V51FQBMLDoxFaByX5rjU9Fb1f7q4eUB4gCUvJoN9kUbw0p/osPlVKUxZucsGpV3B4MRW8z86tgjuoP1gfEuk4O5mb+fNVSvK511c2rl0EIck9oWjeeLcT3a/QJILwqQGCjExoBqgGhC3Pme3w2chAPd53UDd5k4QYxBOmie1Q/YzmM0R37HB6de+oyjAsGNwMM/9ymKTK00wq53SxnLY2kpz78oQApRHusMVfWN9Q0rfRXNdxEFQqCiXsWY+/hWb3P3PJjOn+y4vWsL2VzJyPgmuBmazeOlzzhyXzqcbBaAirnyexlHOW40ZtPa9smLBWSLTAfGgE2Rx+YEJg6aic0gtyo//ksBJD+Rtau8JeaOkxWhrC8d7fYT36WJB0NYmIOU32HxnprrXMTXw6OO4FaaG4DY8QPl5QFSEesASlH6nPX6apy/7GCc32UztOAU01DqFgsTKXDW1H1twVJYkJmbJTfCZxnLx3Z8Pc6XWt244U5L33ux76aRlnQVFeQ8awRRGhFa/PAaWWOPCkqfEPigxQonC3jRt89SYrJIP5ZMR5xu/XlRgASRFcFbgDBIX9LxnUTXJtQWpdAfs6nXC3AYfKwYFu7ZXsWQrZEHU3JeKwOSF7UExK2l0/40NnG4B9htlpmBOQDwvAQd9qk5jZ8uZCUzHDz877ERw21BpdTH9iUteVGABPEV84K51WgcvHuGXcfeFBhhnXo978XAi1Zo1vhEERhKeSYf49LmukvQvgzkFlVNV6RnoCeWhW2T+8HMu8fys2iz2qXZsn933BuEGAkqNCEQ9u/YynGnjfL1jwck+ARjK4pU7nLuJb8LiEzJ37yai87UdZJSPySXQefXriuzUipzXRBksiuR+ZifjOfoKUCK+1UikGe20iACTBulXjRNHSeBrq9LjyF6bMDAhKm9rmQagOhTQjgKIB79OEIHWZU/FhAHJyFB2B19hbiQqOqLPiGvxOULMiWegyHl+TgCXvENm3ZqJ8FM+636zCDmCSZQUrP6S7v6Cdc2douMjlVEa1NPMA5esysTs+p8yX1i/rMZRUv84Rs30vladSklPIf4qsd8548FpDCzOnqIUp3EV86zRGIuYMRdEZBSvTzId5ktwzg1KtsthrjBaPuo+ing+D0AClArjdJM7azPZ3rsIZszfYWaYL/+8sTlL7x+wRal+GlCfJK7YvyhnOJ3gibHEkIhHXdU/uGAbJgjTYWwEP6sARtmyfwa7ZWA55FUjHWAYTaw56Za3IRgXklzV4AwcnIznXvLvGZ9fwxBMLxmVFb8jj5BiXe3pO+2x4byIgW55/irIw9nzrfBMel7/Ef+RwJo1yKxWwTny/f9/f0BKYRV0lgGskVcxfjwD1X98CM1iVca48N9pToKR5noNs/4/RPuabsNmbXjgi8YbvJzG2yb9syPxVu9PNshJwVK4YN8QSjaYI4xR0N83sDC9mwnC0qRoDvqnXv4Ux6aKetGNoHnHOdGuEr1Qnf5zvH3B6TW+YaRtWtbpzWCd+v6XQ8kSzamAAB0qP7iz5jNC0Y+gnG7Oo10izN8gXC51vfaewDgUq7dhDfjGNrBBZ3zhF2SbvhrA4C/ZKp3id+ih+k6d0uYR7SsLlim36XJ3+aK9xgBxuDAupuiIMWX/Ldc//0BqTFZcxGhZKHmC8fdumVwMqIOliC4xu3ORrXFyZiZWKVTc+XHuGyfSdwhG+xu+Jkml3VNBPrDyG5X5QtLvPzWMBvqbtEQNYlHAtze7UFoSJBKm4bK9iF9hQ4TnvWkZxlWobl8D0nYfMknvw8gShNABMEVIJqj/EIMrwMY9yORZauPdl91d+FHlZeZvgAaPytOPdtxhc4fDoifaiqD4rrRS/wME/U1RZ0FZojnndWXMugP4uUd33NfcU9T5WsJbo/tuSavmSKrvGJ34y3XV7waIaiCbVubUsai+VR/EPb4YRvoL+CUugIW4ysXqmfL13K8o7Jceebjxm4yENW4EKry+rLoCEk2RPRVAhkYdp4tOBEqNs+C8UZXbrK+IXIJJ+t2HqS7uXe2PVmUdhkmz9xoZ39It05dk1EvQ37PCykJME7IHPurdEuAWCMct7zOsETL/PhzHbcwuMBQjvW2PJe/eWzbJuiu3kNP3tXwbJvK7XvP900mQbEE1+19rHPACF98Kf/Sjgz0RyvVAO22pWiI/86I9WLmbD1DyV1J47tmTM1x7iIoMn7XXLSZXxwAir/ys+TlnwlvDfujzYI+wVSF/Yds6QhNxZzFrwgFRdt/goaasBVNd8xKR8hILPk+BNZde78PIHf9bZ2F9kBt3oSWZ9GGonfX76TKM6+XDyde2GqvfAlNDCBwUZiZ+GFndr1399FLH0PqPfH9xG71GcLwa/ZZNXnxk7c/fUWY17pdivUHmHmtjWSm5jOYvNvvjlDYvj2UEjDs1Cn3do+/LyAVQwsR4QfI90QEw0uB3Tkby5DcCFOJgjZmhsHojJ0JW0yNxDq2fkHm1AYbWkgdmRIv6HD/pnWunMJc3ksRDD5qkP3u89taDde/+T5gd/3qhNfvuK9TFvwWW4PMoZncjF0lAB0CIyH2bbH/Gg1xtdzj9mZLbFT+8p/fHJCNSYHgGIgMoXhd6fM9Cwc/PCUiqiKV+NcTMCXej+ep6xzggJd2NGfx9hZMjGwvA9/0Uc4r5mh2Iligz0j84bRvefVAhvuL2QdnvPEFL/dfvcI2sd7i7hHmD66BDHDmgujrDApOZJcFjgcC1MJXvpcQ+O5SNk0C8y3a4fO/OSBBZMWgAKRIDtfcxe5AZfJG6vIDwajCaE2QdtzIy8dtTqZ4P46VebINZ9NKcg49WTgiKjPyWhAUKKmGqAFIlxc9MUm+zNkCbDXAjcAdjk37cVMFtBg1lTEU5m7Tyl0J2i1cu+fqbq3Pvv8+gNS7rQGiA78v31SqR6gIMy2amE1A4AXaKWFoYVAAQkQVi1mYIp27gFg3wOPoeoqACIazavfrOpfwp59858N64SvyLh57ysVnec5nf8vy+wPyjKMpTNYnRIFpzjfiVTMYKzCbN6AQdwGToQEcYKl1Xfb0RmpfdeD+vdL+jDR/raln+Qfuv9bJQ/dlqAzYHKuK2u7YpCYDZRLFOnUNKcytbsZBhvqcmuVralnDgtMw2//jD3U5MgGM8NikISCWGX/Qklujjif8KXSUY7n/Gxz/UEBiPA7ynoFlXsTfzd0Cjhfuu18qZvCoIYi5Zr6VH6qqZfACWGgI7XiAFh/4Un9Vg89y+ENNVjC4SF3FjM3A1R5KkdisEUJXwVc0yysylFK0SGn/elHjMlyhUZq9Qkt5OJrlj7Ts0FOqPPfx/wO9EHqXy50V4wAAAABJRU5ErkJggg==\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "image_resized = explainer.visualisation.resize_PILimage(images[1], 100)\n", "from IPython.display import display\n", "display(image_resized)" ] }, { "cell_type": "markdown", "id": "933d4dca", "metadata": {}, "source": [ "## With a time series dataset" ] }, { "cell_type": "markdown", "id": "ef3dbe31", "metadata": {}, "source": [ "The [ArrowHead](https://www.timeseriesclassification.com/description.php?Dataset=ArrowHead) dataset consists of outlines of the images of arrowheads. The shapes of the projectile points are converted into a time series using the angle-based method. The classes (called \"Avonlea\", \"Clovis\" and \"Mix\") are based on shape distinctions such as the presence and location of a notch in the arrow.\n", "\n", "\"arrow-heads\"" ] }, { "cell_type": "markdown", "id": "ddce51dc", "metadata": {}, "source": [ "Note that we need the sktime package to load this dataset: ```python3 -m pip install sktime```\n", "We start by preparing the data:" ] }, { "cell_type": "code", "execution_count": 10, "id": "bab10940", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "n_instances: 211\n", "n_features: 251\n", " 0 1 2 3 4 5 6 \n", "0 -1.963009 -1.957825 -1.956145 -1.938289 -1.896657 -1.869857 -1.838705 \\\n", "1 -1.774571 -1.774036 -1.776586 -1.730749 -1.696268 -1.657377 -1.636227 \n", "2 -1.866021 -1.841991 -1.835025 -1.811902 -1.764390 -1.707687 -1.648280 \n", "3 -2.073758 -2.073301 -2.044607 -2.038346 -1.959043 -1.874494 -1.805619 \n", "4 -1.746255 -1.741263 -1.722741 -1.698640 -1.677223 -1.630356 -1.579440 \n", ".. ... ... ... ... ... ... ... \n", "206 -1.625142 -1.622988 -1.626062 -1.610799 -1.568567 -1.552384 -1.535922 \n", "207 -1.657757 -1.664673 -1.632645 -1.611002 -1.591838 -1.548710 -1.502004 \n", "208 -1.603279 -1.587365 -1.577407 -1.556043 -1.531201 -1.505051 -1.487764 \n", "209 -1.739020 -1.741534 -1.732863 -1.722299 -1.698109 -1.665590 -1.618474 \n", "210 -1.630727 -1.629918 -1.620556 -1.607832 -1.579719 -1.562561 -1.526980 \n", "\n", " 7 8 9 ... 242 243 244 \n", "0 -1.812289 -1.736433 -1.673329 ... -1.655329 -1.719153 -1.750881 \\\n", "1 -1.609807 -1.543439 -1.486174 ... -1.484666 -1.539972 -1.590150 \n", "2 -1.582643 -1.531502 -1.493609 ... -1.652337 -1.684565 -1.743972 \n", "3 -1.731043 -1.712653 -1.628022 ... -1.743732 -1.819801 -1.858136 \n", "4 -1.551225 -1.473980 -1.459377 ... -1.607101 -1.635137 -1.686346 \n", ".. ... ... ... ... ... ... ... \n", "206 -1.498036 -1.460416 -1.430095 ... -1.447404 -1.476224 -1.531105 \n", "207 -1.447861 -1.389342 -1.322178 ... -1.580908 -1.631919 -1.664673 \n", "208 -1.455712 -1.419410 -1.391145 ... -1.492721 -1.511071 -1.539271 \n", "209 -1.585797 -1.547740 -1.504280 ... -1.487935 -1.536131 -1.594358 \n", "210 -1.507067 -1.464752 -1.417547 ... -1.350127 -1.403990 -1.451258 \n", "\n", " 245 246 247 248 249 250 label \n", "0 -1.796273 -1.841345 -1.884289 -1.905393 -1.923905 -1.909153 0 \n", "1 -1.635663 -1.639989 -1.678683 -1.729227 -1.775670 -1.789324 1 \n", "2 -1.799117 -1.829069 -1.875828 -1.862512 -1.863368 -1.846493 2 \n", "3 -1.886146 -1.951247 -2.012927 -2.026963 -2.073405 -2.075292 0 \n", "4 -1.691274 -1.716886 -1.740726 -1.743442 -1.762729 -1.763428 1 \n", ".. ... ... ... ... ... ... ... \n", "206 -1.551077 -1.567998 -1.597641 -1.623653 -1.622988 -1.624174 2 \n", "207 -1.672895 -1.678613 -1.666825 -1.667466 -1.681769 -1.678613 2 \n", "208 -1.565389 -1.577493 -1.585284 -1.603086 -1.605382 -1.605597 2 \n", "209 -1.615147 -1.636495 -1.669889 -1.699870 -1.713118 -1.728176 2 \n", "210 -1.472321 -1.513637 -1.550431 -1.581576 -1.595273 -1.620783 2 \n", "\n", "[211 rows x 252 columns]\n" ] } ], "source": [ "import pandas\n", "from sktime.datasets import load_arrow_head\n", "\n", "X, y = load_arrow_head()\n", "\n", "rawdata = []\n", "n_instances = X[\"dim_0\"].shape[0]\n", "n_features = len(X[\"dim_0\"][0])\n", "print(\"n_instances:\", n_instances)\n", "print(\"n_features:\", n_features)\n", "\n", "for i in range(n_instances):\n", " instance = X[\"dim_0\"][i]\n", " label = y[i]\n", " rawdata.append(list(instance)+[label])\n", " \n", "dataframe = pandas.DataFrame(rawdata)\n", "dataframe.columns = [str(i) for i in range(n_features)]+[\"label\"]\n", "print(dataframe)\n", " " ] }, { "cell_type": "markdown", "id": "6e35780a", "metadata": {}, "source": [ "We create a model using the hold-out approach (by default, the test size is set to 30%). We choose to use a Boosted Tree by using XGBoost. " ] }, { "cell_type": "code", "execution_count": 11, "id": "acd9da14", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "data:\n", " 0 1 2 3 4 5 6 \n", "0 -1.963009 -1.957825 -1.956145 -1.938289 -1.896657 -1.869857 -1.838705 \\\n", "1 -1.774571 -1.774036 -1.776586 -1.730749 -1.696268 -1.657377 -1.636227 \n", "2 -1.866021 -1.841991 -1.835025 -1.811902 -1.764390 -1.707687 -1.648280 \n", "3 -2.073758 -2.073301 -2.044607 -2.038346 -1.959043 -1.874494 -1.805619 \n", "4 -1.746255 -1.741263 -1.722741 -1.698640 -1.677223 -1.630356 -1.579440 \n", ".. ... ... ... ... ... ... ... \n", "206 -1.625142 -1.622988 -1.626062 -1.610799 -1.568567 -1.552384 -1.535922 \n", "207 -1.657757 -1.664673 -1.632645 -1.611002 -1.591838 -1.548710 -1.502004 \n", "208 -1.603279 -1.587365 -1.577407 -1.556043 -1.531201 -1.505051 -1.487764 \n", "209 -1.739020 -1.741534 -1.732863 -1.722299 -1.698109 -1.665590 -1.618474 \n", "210 -1.630727 -1.629918 -1.620556 -1.607832 -1.579719 -1.562561 -1.526980 \n", "\n", " 7 8 9 ... 242 243 244 \n", "0 -1.812289 -1.736433 -1.673329 ... -1.655329 -1.719153 -1.750881 \\\n", "1 -1.609807 -1.543439 -1.486174 ... -1.484666 -1.539972 -1.590150 \n", "2 -1.582643 -1.531502 -1.493609 ... -1.652337 -1.684565 -1.743972 \n", "3 -1.731043 -1.712653 -1.628022 ... -1.743732 -1.819801 -1.858136 \n", "4 -1.551225 -1.473980 -1.459377 ... -1.607101 -1.635137 -1.686346 \n", ".. ... ... ... ... ... ... ... \n", "206 -1.498036 -1.460416 -1.430095 ... -1.447404 -1.476224 -1.531105 \n", "207 -1.447861 -1.389342 -1.322178 ... -1.580908 -1.631919 -1.664673 \n", "208 -1.455712 -1.419410 -1.391145 ... -1.492721 -1.511071 -1.539271 \n", "209 -1.585797 -1.547740 -1.504280 ... -1.487935 -1.536131 -1.594358 \n", "210 -1.507067 -1.464752 -1.417547 ... -1.350127 -1.403990 -1.451258 \n", "\n", " 245 246 247 248 249 250 label \n", "0 -1.796273 -1.841345 -1.884289 -1.905393 -1.923905 -1.909153 0 \n", "1 -1.635663 -1.639989 -1.678683 -1.729227 -1.775670 -1.789324 1 \n", "2 -1.799117 -1.829069 -1.875828 -1.862512 -1.863368 -1.846493 2 \n", "3 -1.886146 -1.951247 -2.012927 -2.026963 -2.073405 -2.075292 0 \n", "4 -1.691274 -1.716886 -1.740726 -1.743442 -1.762729 -1.763428 1 \n", ".. ... ... ... ... ... ... ... \n", "206 -1.551077 -1.567998 -1.597641 -1.623653 -1.622988 -1.624174 2 \n", "207 -1.672895 -1.678613 -1.666825 -1.667466 -1.681769 -1.678613 2 \n", "208 -1.565389 -1.577493 -1.585284 -1.603086 -1.605382 -1.605597 2 \n", "209 -1.615147 -1.636495 -1.669889 -1.699870 -1.713118 -1.728176 2 \n", "210 -1.472321 -1.513637 -1.550431 -1.581576 -1.595273 -1.620783 2 \n", "\n", "[211 rows x 252 columns]\n", "-------------- Information ---------------\n", "Dataset name: pandas.core.frame.DataFrame\n", "nFeatures (nAttributes, with the labels): 252\n", "nInstances (nObservations): 211\n", "nLabels: 3\n", "--------------- Evaluation ---------------\n", "method: HoldOut\n", "output: BT\n", "learner_type: Classification\n", "learner_options: {'seed': 0, 'max_depth': None, 'eval_metric': 'mlogloss'}\n", "--------- Evaluation Information ---------\n", "For the evaluation number 0:\n", "metrics:\n", " micro_averaging_accuracy: 89.58333333333334\n", " micro_averaging_precision: 84.375\n", " micro_averaging_recall: 84.375\n", " macro_averaging_accuracy: 89.58333333333334\n", " macro_averaging_precision: 84.61147421931736\n", " macro_averaging_recall: 85.05072463768116\n", " true_positives: {'0': 18, '1': 22, '2': 14}\n", " true_negatives: {'0': 37, '1': 36, '2': 45}\n", " false_positives: {'0': 2, '1': 5, '2': 3}\n", " false_negatives: {'0': 7, '1': 1, '2': 2}\n", " accuracy: 84.375\n", " sklearn_confusion_matrix: [[18, 4, 3], [1, 22, 0], [1, 1, 14]]\n", "nTraining instances: 147\n", "nTest instances: 64\n", "\n", "--------------- Explainer ----------------\n", "For the evaluation number 0:\n", "**Boosted Tree model**\n", "NClasses: 3\n", "nTrees: 300\n", "nVariables: 345\n", "\n", "--------------- Instances ----------------\n", "number of instances selected: 1\n", "----------------------------------------------\n" ] } ], "source": [ "from pyxai import Learning, Explainer, Tools\n", "\n", "\n", "# Machine learning part\n", "learner = Learning.Xgboost(dataframe, learner_type=Learning.CLASSIFICATION)\n", "model = learner.evaluate(method=Learning.HOLD_OUT, output=Learning.BT)\n", "instance, prediction = learner.get_instances(model, n=1, correct=True, predictions=[0])" ] }, { "cell_type": "markdown", "id": "6bfe4fea", "metadata": {}, "source": [ "Next, we calculate an explanation for an arrow (an instance) that is classified as 0 (corresponding to the Avonlea era). We have fix the time limit to 60 seconds, but we obtain the same result with 10 seconds. " ] }, { "cell_type": "code", "execution_count": 12, "id": "82e9c6be", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "tree_specific_reason: 38\n" ] } ], "source": [ "# Explanation part\n", "explainer = Explainer.initialize(model, instance)\n", "tree_specific_reason = explainer.tree_specific_reason(time_limit=60)\n", "print(\"tree_specific_reason:\", len(tree_specific_reason))" ] }, { "cell_type": "markdown", "id": "9ca574e0", "metadata": {}, "source": [ "If we display in a Jupyter notebook this explanation, it is difficult to interpret. " ] }, { "cell_type": "code", "execution_count": 13, "id": "186e79d8", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "explainer.visualisation.notebook(instance, tree_specific_reason)" ] }, { "cell_type": "markdown", "id": "d05869b7", "metadata": {}, "source": [ "To overcome this problem, we display it as a time series. " ] }, { "cell_type": "code", "execution_count": 14, "id": "fc331c01", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "explainer.visualisation.notebook(instance, tree_specific_reason, time_series={\"serie\":learner.feature_names[:-1]}, width=600)" ] }, { "cell_type": "markdown", "id": "95b3225d", "metadata": {}, "source": [ "The red line represents the instance values. The two blue lines represent the extreme values of the explanation for each feature. A blue value at the top (resp. at the bottom) means that the extreme value for the associated feature is $+\\infty$ (resp. $-\\infty$).\n", "\n", "The explanation shows us that all the other instances (other lines we can imagine) between the two blue lines will have the same classification. In our case, we clearly see that the explanation tells us that it is because of the two bumps on each side that this instance is classified as such." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "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": "3.11.6" } }, "nbformat": 4, "nbformat_minor": 5 }