diff --git a/eis_toolkit/prediction/weights_of_evidence.py b/eis_toolkit/prediction/weights_of_evidence.py index 0aeef6e9..66bde370 100644 --- a/eis_toolkit/prediction/weights_of_evidence.py +++ b/eis_toolkit/prediction/weights_of_evidence.py @@ -408,3 +408,77 @@ def weights_of_evidence_calculate_responses( confidence_array = posterior_probabilities / posterior_probabilities_std return posterior_probabilities, posterior_probabilities_std, confidence_array + + +@beartype +def agterberg_cheng_CI_test( + posterior_probabilities: np.ndarray, posterior_probabilities_std: np.ndarray, nr_of_deposits: int +) -> Tuple[bool, bool, bool, float, str]: + """Perform the conditional independence test presented by Agterberg-Cheng (2002). + + Agterberg, F. P. & Cheng, Q. (2002). Conditional Independence Test for Weights-of-Evidence Modeling. + Natural Resources Research. 11. 249-255. + + Args: + posterior_probabilities: Array of posterior probabilites. + posterior_probabilities_std: Array of standard deviations in the posterior probability calculations. + nr_of_deposits: Number of deposit pixels in the input data for weights of evidence calculations. + Returns: + Whether the conditional hypothesis can be accepted for the evidence layers that the input + posterior probabilities and standard deviations of posterior probabilities are calculated from. + Whether the probability satisfies the 99% confidence limit. + Whether the probability satisfies the 95% confidence limit. + Ratio T/n. Results > 1, may be because of lack of conditional independence of layers. + T should not exceed n by more than 15% (Bonham-Carter 1994, p. 316). + A summary of the the conditional independence calculations. + + Raises: + InvalidParameterValueException: Value of nr_of_deposits is not at least 1. + """ + if nr_of_deposits < 1: + raise InvalidParameterValueException("Expected input deposits count to be at least 1.") + + # One-tailed significance test according to Agterberg-Cheng (2002): + # Conditional independence must satisfy: + # T - n < 1.645 * s(T) with a probability of 95% + # T - n < 2.33 * s(T) with a probability of 99%, + # where + # T = the sum of posterior probabilities in all unit cells in the study area + # n = total number of deposits + + T = np.nansum(posterior_probabilities) + + ratio = T / nr_of_deposits + ratio_msg = "T / n > 1 may suggest lack of conditional independence.\n" + ratio_msg_bonham_carter = "According to Bonham-Carter (1994), T / n should not exceed 1.15.\n" + + difference = T - nr_of_deposits + + T_std = np.sqrt(np.nansum(posterior_probabilities_std)) + + confidence_limit_99 = 2.33 * T_std + confidence_limit_95 = 1.645 * T_std + + confidence_99 = bool(difference < confidence_limit_99) + confidence_95 = bool(difference < confidence_limit_95) + sign_99 = "<" if confidence_99 else ">" + sign_95 = "<" if confidence_95 else ">" + + conditional_independence = confidence_99 and confidence_95 + + summary = f""" + Results of conditional independence test:\n\n + Observed number of deposits, n: {nr_of_deposits}\n + Expected number of deposits, T: {T}\n + Standard deviation of the expected number of deposits, s(T): {T_std}\n + T - n = {difference}\n + T / n = {ratio}\n{ratio_msg if ratio > 1 else ""}{ratio_msg_bonham_carter if ratio > 1.15 else ""} + Agterberg & Cheng CI test:\n + Data {"satisfies" if confidence_99 else "does not satisfy"} condition T - n < 2.33 * s(T):\n + {difference} {sign_99} {confidence_limit_99}\n + Data {"satisfies" if confidence_95 else "does not satisfy"} condition T - n < 1.645 * s(T):\n + {difference} {sign_95} {confidence_limit_95}\n + {"Conditional independence hypothesis should be rejected" if not conditional_independence else ""} + """ + + return conditional_independence, confidence_99, confidence_95, ratio, summary diff --git a/notebooks/weights_of_evidence.ipynb b/notebooks/weights_of_evidence.ipynb index 62bddc71..c56ad0dc 100644 --- a/notebooks/weights_of_evidence.ipynb +++ b/notebooks/weights_of_evidence.ipynb @@ -3,8 +3,19 @@ { "cell_type": "code", "execution_count": 1, - "metadata": {}, - "outputs": [], + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/root/.cache/pypoetry/virtualenvs/eis-toolkit-QEzTY9B6-py3.10/lib/python3.10/site-packages/geopandas/_compat.py:112: UserWarning: The Shapely GEOS version (3.10.3-CAPI-1.16.1) is incompatible with the GEOS version PyGEOS was compiled with (3.10.4-CAPI-1.16.2). Conversions between both will be slow.\n", + " warnings.warn(\n" + ] + } + ], "source": [ "import rasterio\n", "from matplotlib import pyplot as plt\n", @@ -14,22 +25,16 @@ "import sys\n", "sys.path.insert(0, \"..\")\n", "\n", - "from eis_toolkit.prediction.weights_of_evidence import weights_of_evidence_calculate_weights, weights_of_evidence_calculate_responses" + "from eis_toolkit.prediction.weights_of_evidence import agterberg_cheng_CI_test, weights_of_evidence_calculate_weights, weights_of_evidence_calculate_responses" ] }, { "cell_type": "code", "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "108 1\n" - ] - } - ], + "metadata": { + "tags": [] + }, + "outputs": [], "source": [ "with rasterio.open(\"../tests/data/remote/wofe/wofe_evidence_raster.tif\") as evidence_raster:\n", " deposits = gpd.read_file(\"../tests/data/remote/wofe/wofe_deposits.shp\")\n", @@ -43,7 +48,31 @@ { "cell_type": "code", "execution_count": 3, - "metadata": {}, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "16" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "nr_of_deposits" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "tags": [] + }, "outputs": [ { "data": { @@ -209,7 +238,7 @@ "7 0.0000 0.0000 0.0000 " ] }, - "execution_count": 3, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -221,8 +250,10 @@ }, { "cell_type": "code", - "execution_count": 4, - "metadata": {}, + "execution_count": 5, + "metadata": { + "tags": [] + }, "outputs": [ { "data": { @@ -425,7 +456,7 @@ "7 -0.8055 1.0047 " ] }, - "execution_count": 4, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -437,8 +468,10 @@ }, { "cell_type": "code", - "execution_count": 5, - "metadata": {}, + "execution_count": 6, + "metadata": { + "tags": [] + }, "outputs": [ { "data": { @@ -641,7 +674,7 @@ "7 -0.3994 0.3806 " ] }, - "execution_count": 5, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -653,8 +686,10 @@ }, { "cell_type": "code", - "execution_count": 6, - "metadata": {}, + "execution_count": 7, + "metadata": { + "tags": [] + }, "outputs": [ { "data": { @@ -857,7 +892,7 @@ "7 -0.0501 0.2608 " ] }, - "execution_count": 6, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -869,8 +904,10 @@ }, { "cell_type": "code", - "execution_count": 7, - "metadata": {}, + "execution_count": 8, + "metadata": { + "tags": [] + }, "outputs": [], "source": [ "colormap_name = \"jet\"" @@ -878,8 +915,10 @@ }, { "cell_type": "code", - "execution_count": 8, - "metadata": {}, + "execution_count": 9, + "metadata": { + "tags": [] + }, "outputs": [ { "data": { @@ -887,13 +926,13 @@ "" ] }, - "execution_count": 8, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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" ] @@ -925,8 +964,10 @@ }, { "cell_type": "code", - "execution_count": 9, - "metadata": {}, + "execution_count": 10, + "metadata": { + "tags": [] + }, "outputs": [ { "data": { @@ -934,13 +975,13 @@ "" ] }, - "execution_count": 9, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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cnHzH9p988onat28vX19fnTt3zv6KiIhQenq6vv32W0nSypUrVaZMGQ0ePNje1s3Nzf4fuwynT5/W7t27NWDAAIf/nDRu3FidOnXSihUrTDHkZlbJsmXLZLPZNG7cOLm5Of4quX167e3749KlSzp37pzat2+vq1ev6uDBg3fs53ZVq1ZV/fr17ftg8+bNcnd314svvqgzZ87o8OHDkm7+N61du3b2OD755BOFhISofv36Dvv0gQcekCStX78+2z5XrlypsLAwNW3a1F7m5+envn37Zlm/QYMGat++vUPM9erV07Fjx+44vozLv5YvX67r16/fsX5e3X69vXRzP1avXl01atRQ/fr15efnZ586nd3NGW/fj+fOndPVq1dls9lM5ZmnuAMAijfOjbLHuVHxPTcaP368Fi1apGbNmunrr7/Wq6++qhYtWqh58+amSxbvJOOcKWN21+bNm9WiRQt5enoqLCzMfklaxjovLy+1bNnS3j6786nr16+bygtynwAZSmXS6Ntvv1W3bt0UFBQki8WS4/TL7BiGoalTp+ree++V1WpV9erV9frrr+d/sHApb29vSTc/0HNr8+bNioiIsF8fX7VqVfujynNzYnT48GGtXLlSVatWdXhFRERI+u1mfPHx8apWrZrKlSvn0L5u3boOy/Hx8ZKkevXqmfoKCQnRuXPndOXKFYfy2rVr3zHOo0ePys3NTQ0aNMix3r59+/TII4/Ix8dH3t7eqlq1qv7yl79Iyt3+yKx9+/b2D+GNGzeqZcuWatmypfz8/LRx40alpKToxx9/dDg5OXz4sPbt22fap/fee6+knG9wGB8fb9qnknk/Z6hRo4apzNfXVxcvXrzj2MLDw9WzZ0+NHz9eVapUUffu3RUXF3fHxEtycrISExPtrwsXLuRYv2HDhqpUqZJDYijjOn6LxaKwsDCHdcHBwaZxZd6XU6ZM0cmTJ03l//73v+84bgDFA+dPkDg3ygnnRsX33EiS+vTpo40bN+rixYtatWqVnnjiCe3atUvdunXTtWvX7tg+Q9u2bWWxWLI8z6pUqZIaNGjgsO4Pf/iDPD097e0TEhJMx2Xx4sXasmWLqTzzvZOAglAq72l05coVNWnSRIMGDdKf//znPG1jxIgRWrVqlaZOnapGjRrpwoULufplhOLF29tbQUFBpsdmZufo0aN68MEHVb9+fU2bNk3BwcHy9PTUihUrNH36dNPNGrNis9nUqVMnvfTSS1muz/gwL0i3/wfs90hKSlJ4eLi8vb01YcIE1alTR15eXtq5c6defvnlXO2PzNq1a6e5c+fq2LFj2rhxo9q3by+LxaJ27dpp48aNCgoKks1mczgxstlsatSokaZNm5blNoODg/M8xszc3d2zLDdyuP9BBovFoiVLlui7777TF198oa+//lqDBg3SW2+9pe+++04VKlTIst2IESP0wQcf2JfDw8NzfLqGm5ubwsLCtGXLFhmGoc2bN9tP3iWpTZs2mj9/vv1eRz169DBtY/Xq1Q7LH374oVatWqWFCxc6lN933313HDeA4oHzJ0icG/1enBv9piidG93O29tbnTp1UqdOnVSmTBl98MEH2rZtm8LDw3PVvnLlyqpfv742bdqky5cv66efflJ0dLR9fZs2bbRp0yb98ssvSkhIMM3QCgwMNJ1nTfl/9u49Lsoy///4exABDwziAZHEs5mW574qqKuVZmYlHXQrNzXDbLPzluW3g6j7DUvLtlatNHE3K3ftp2RtmZpaidSahzxUlqZQJnZQQDwAwvX7Q5kc7kGYkZlh4PV8PHiMc9/XdV+f674Z5uNn7rnvmTOVlZWl5557zml5165dKxQTcD5qZNFo6NChGjp0aJnr8/Pz9fjjj+utt95Sdna2LrnkEj3zzDOOOzh8/fXXmjdvnnbu3On4hKIinz4gMF1zzTV69dVXlZ6erri4uHO2fffdd5Wfn68VK1Y4fari6hTfsu6Q0LZtW+Xl5Tk+PStLy5YttW7dOh0/ftzpE7U9e/ZY2knS7t27Ldv45ptv1LhxY9WrV++cY5UVZ3Fxsb766iun05PPtn79ev32229atmyZ48KL0ulb9XqqJOFZvXq1Nm3apMcee0zS6Qs7zps3TzExMapXr5569uzpFOuXX36pK664wu07U7Rs2dKyTyXrfnZHeTH06dNHffr00f/93//pzTff1KhRo7RkyRIlJia6bD9p0iTHJ5TS6U/vytOvXz998MEHWrFihX7++WenO4bEx8fr8ccf1/vvv68TJ064vDhj6d/PDRs2KCwsrNzfWwCBi/wJJciNXCM3CuzcyJVLL71U//jHP3Tw4EG3+vXr108LFy7UqlWrVFRU5HTR+Pj4eL311luOIlbpPMtVPrV48WLl5+eTZ8EvauTX08pzzz33KD09XUuWLNH27ds1YsQIXXXVVY7vBJfcMeK9995T69at1apVKyUmJvJJWTU1adIk1atXT4mJiTp06JBl/d69e/W3v/1N0u+fpJz9yUlOTo5SUlIs/erVq6fs7GzL8pEjRyo9PV0ffvihZV12drZOnTolSRoyZIgKCws1f/58x/ri4mLNmTPHqU+zZs3UrVs3/eMf/3Aab+fOnVq1apWuvvrqc8y+bAkJCQoKCtK0adMsn4qVzN/V/igoKNDcuXM9GlM6/R+MCy64QLNnz1ZhYaGj2NG/f3/t3btXb7/9tvr06aPg4N9r4iNHjtSBAwec9lWJEydOWE5BP9uQIUOUnp6ubdu2OZYdPnxYb7zxhsdzKElESx//I0eOWD51K0k6z3UadqdOnTRo0CDHz9lJYVlKEpRnnnlGdevWdUpue/XqpeDgYD377LNObQHgXMifag5yI9fIjQIzNzp+/LjS09Ndrvvggw8kuf4q47n069dPRUVFmjVrltq3b68mTZo41sXHxysvL09z585VUFBQhe9CCPhLjTzT6FwyMzOVkpKizMxMxcTESJIefvhhrVy5UikpKXr66af1/fffKyMjQ0uXLtU///lPFRUV6cEHH9RNN92ktWvX+nkGqGxt27bVm2++qT/+8Y/q2LGjRo8erUsuuUQFBQXauHGjli5dqrFjx0qSrrzySoWEhOjaa6/VhAkTlJeXp/nz5ysqKsryCUXPnj01b948/fWvf1W7du0UFRWlyy+/XI888ohWrFiha665xnE70mPHjmnHjh16++23tX//fjVu3FgJCQnq1auX/vKXv2jPnj266KKLtGLFCkfyffYnNjNnztTQoUMVFxenO+64w3Fb2YiICCUlJXm0X9q1a6fHH39c06dPV//+/XXDDTcoNDRUmzZtUkxMjJKTkxUfH6/IyEiNGTNG9913n2w2m15//fUKnY58Lv3799eSJUvUuXNnxydHPXr0UL169fTtt9/q1ltvdWp/22236d///rfuuusurVu3Tn379lVRUZG++eYb/fvf/9aHH37odAHCs02aNEmLFy/W4MGDde+99zpuK9uiRQsdPnzY7U/npNPJTq1atfTMM88oJydHoaGhuvzyy/Xmm29q7ty5uv7669W2bVsdPXpU8+fPl91u9ziBLUuvXr0UEhKi9PR0DRw40CmRrFu3rrp27ar09HQ1aNDAcQtfACgL+VPNQm7kGrlRYOZGx48fV3x8vPr06aOrrrpKsbGxys7OVmpqqj799FMlJCSoe/fubm2z5AO39PR0x2uhxIUXXqjGjRsrPT1dnTt3dlzsG6iy/HDHtipFklm+fLnjecntpevVq+f0ExwcbEaOHGmMMWb8+PFGktm9e7ej3+bNm40k88033/h6CvCRb7/91owfP960atXKhISEmPDwcNO3b1/z0ksvOd0edcWKFaZLly4mLCzMtGrVyjzzzDNm4cKFlluQZmVlmWHDhpnw8HAjyek2mkePHjWTJ0827dq1MyEhIaZx48YmPj7ezJo1yxQUFDja/fLLL+bWW2814eHhJiIiwowdO9akpaUZSWbJkiVO8a9Zs8b07dvX1KlTx9jtdnPttdear776yqlNya1jf/nlF8v8S99WtsTChQtN9+7dTWhoqImMjDQDBgwwq1evdqxPS0szffr0MXXq1DExMTFm0qRJ5sMPP7TcVrUit5UtUXLL+D//+c9OywcNGmQkmY8++sjSp6CgwDzzzDPm4osvdsTas2dPM3XqVJOTk+NoV/q2ssYYs3XrVtO/f38TGhpqmjdvbpKTk82LL75oJJmsrCynvsOGDbOMPWDAAMttUufPn2/atGljatWq5dgXW7ZsMbfccotp0aKFCQ0NNVFRUeaaa64xX3zxRYX2i7vi4uKMJPO///u/lnX33XefkWSGDh1aoW1NmTKlwscPQOAjf4Ix5EbkRtUjNyosLDTz5883CQkJpmXLliY0NNTUrVvXdO/e3cycOdPk5+d7tN2YmBgjybz66quWddddd53L41WWMWPGWPYX4Cs2Y86zrB3gbDabli9f7rjQ67/+9S+NGjVKu3btsly0rX79+oqOjtaUKVP09NNPO93i8MSJE6pbt65WrVqlwYMH+3IKgJPU1FRdf/312rBhg9N1alC5HnjgAb3yyivKy8sr8wKPAFBdkT8hkJAb+Qa5EVA98fW0Urp3766ioiL9/PPPTncYOFvfvn116tQp7d27V23btpUkffvtt5J+v7Ae4AsnTpxwuptHUVGRXnrpJdntdvXo0cOPkVUvpffzb7/9ptdff139+vUjKQIAkT+h6iA38g1yI6DmqJFFo7y8PKer++/bt0/btm1Tw4YNdeGFF2rUqFEaPXq0nnvuOXXv3l2//PKLPvroI3Xp0kXDhg3ToEGD1KNHD40bN04vvPCCiouLNXHiRA0ePNgnt/wEStx77706ceKE4uLilJ+fr2XLlmnjxo16+umnK+3WsJDi4uI0cOBAdezYUYcOHdJrr72m3NxcPfnkk/4ODQB8hvwJgYDcyDdqYm70yy+/qKioqMz1ISEhatiwoQ8jAnzE39+P84d169YZSZafku/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" ] @@ -982,8 +1023,10 @@ }, { "cell_type": "code", - "execution_count": 10, - "metadata": {}, + "execution_count": 11, + "metadata": { + "tags": [] + }, "outputs": [ { "data": { @@ -991,13 +1034,13 @@ "" ] }, - "execution_count": 10, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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" ] @@ -1039,8 +1082,10 @@ }, { "cell_type": "code", - "execution_count": 11, - "metadata": {}, + "execution_count": 12, + "metadata": { + "tags": [] + }, "outputs": [ { "data": { @@ -1048,13 +1093,13 @@ "" ] }, - "execution_count": 11, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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" ] @@ -1103,8 +1148,10 @@ }, { "cell_type": "code", - "execution_count": 12, - "metadata": {}, + "execution_count": 13, + "metadata": { + "tags": [] + }, "outputs": [], "source": [ "# Say we have 2 different weight calculations that can be used for posterior probabilities calculations\n", @@ -1118,8 +1165,10 @@ }, { "cell_type": "code", - "execution_count": 13, - "metadata": {}, + "execution_count": 14, + "metadata": { + "tags": [] + }, "outputs": [ { "data": { @@ -1127,13 +1176,13 @@ "" ] }, - "execution_count": 13, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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" ] @@ -1165,8 +1214,10 @@ }, { "cell_type": "code", - "execution_count": 14, - "metadata": {}, + "execution_count": 15, + "metadata": { + "tags": [] + }, "outputs": [], "source": [ "import numpy as np\n", @@ -1186,11 +1237,61 @@ " with rasterio.open(posterior_raster_conf, \"w\", **raster_meta) as dest:\n", " dest.write(posterior_confidence, 1)" ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " Results of conditional independence test:\n", + "\n", + "\n", + " Observed number of deposits, n: 16\n", + "\n", + " Expected number of deposits, T: 15.76794227613399\n", + "\n", + " Standard deviation of the expected number of deposits, s(T): 2.906223209503864\n", + "\n", + " T - n = -0.23205772386600998\n", + "\n", + " T / n = 0.9854963922583744\n", + "\n", + " Agterberg & Cheng CI test:\n", + "\n", + " Data satisfies condition T - n < 2.33 * s(T):\n", + "\n", + " -0.23205772386600998 < 6.771500078144003\n", + "\n", + " Data satisfies condition T - n < 1.645 * s(T):\n", + "\n", + " -0.23205772386600998 < 4.780737179633856\n", + "\n", + " \n", + " \n" + ] + } + ], + "source": [ + "ci_overall, ci_99, ci_95, ratio, summary = agterberg_cheng_CI_test(posterior_array, posterior_array_std, nr_of_deposits)\n", + "print(summary)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { "kernelspec": { - "display_name": "eis_toolkit", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -1205,9 +1306,8 @@ "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.12" - }, - "orig_nbformat": 4 + } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 }