From 5878c408ddc2385c92b07cdf709c741eec272bae Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Jos=C3=A9=20Cabrero-Holgueras?= Date: Wed, 7 Aug 2024 10:37:07 +0000 Subject: [PATCH] fix: set version of notebooks automatically to latest --- examples/complex_model/complex_model.ipynb | 2 +- examples/conv_net/conv_net.ipynb | 2 +- .../linear_regression/linear_regression.ipynb | 56 +++---- examples/neural_net/neural_net.ipynb | 36 ++--- examples/spam_detection/spam_detection.ipynb | 152 +++++++++--------- examples/time_series/time_series.ipynb | 36 ++--- 6 files changed, 142 insertions(+), 142 deletions(-) diff --git a/examples/complex_model/complex_model.ipynb b/examples/complex_model/complex_model.ipynb index 91a3ad3..1bdae2f 100644 --- a/examples/complex_model/complex_model.ipynb +++ b/examples/complex_model/complex_model.ipynb @@ -60,7 +60,7 @@ }, "outputs": [], "source": [ - "%pip install nada-ai~=0.3.0 --quiet" + "%pip install nada-ai --quiet" ] }, { diff --git a/examples/conv_net/conv_net.ipynb b/examples/conv_net/conv_net.ipynb index 895a426..b080d1f 100644 --- a/examples/conv_net/conv_net.ipynb +++ b/examples/conv_net/conv_net.ipynb @@ -84,7 +84,7 @@ } ], "source": [ - "%pip install nada-ai~=0.3.0 --quiet" + "%pip install nada-ai --quiet" ] }, { diff --git a/examples/linear_regression/linear_regression.ipynb b/examples/linear_regression/linear_regression.ipynb index b0fc8bb..f8bdcbd 100644 --- a/examples/linear_regression/linear_regression.ipynb +++ b/examples/linear_regression/linear_regression.ipynb @@ -56,16 +56,16 @@ "cell_type": "code", "execution_count": 1, "metadata": { - "id": "o4PXBlE2v_7K", - "outputId": "943693f2-51f8-4685-ea29-07b89b7b876a", "colab": { "base_uri": "https://localhost:8080/" - } + }, + "id": "o4PXBlE2v_7K", + "outputId": "943693f2-51f8-4685-ea29-07b89b7b876a" }, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m61.0/61.0 kB\u001b[0m \u001b[31m1.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m54.8/54.8 kB\u001b[0m \u001b[31m3.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", @@ -84,34 +84,34 @@ } ], "source": [ - "%pip install nada-ai~=0.3.0 --quiet" + "%pip install nada-ai --quiet" ] }, { "cell_type": "code", + "execution_count": 2, + "metadata": { + "id": "IltP23dY9Wuq" + }, + "outputs": [], "source": [ "import os\n", "import time\n", "import sys" - ], - "metadata": { - "id": "IltP23dY9Wuq" - }, - "execution_count": 2, - "outputs": [] + ] }, { "cell_type": "code", + "execution_count": 3, + "metadata": { + "id": "-qsIYfic9X3T" + }, + "outputs": [], "source": [ "# Configure telemetry settings\n", "enable_telemetry = True #@param {type:\"boolean\"}\n", "my_identifier = \"your-telemetry-identifier\" #@param {type:\"string\"}" - ], - "metadata": { - "id": "-qsIYfic9X3T" - }, - "execution_count": 3, - "outputs": [] + ] }, { "cell_type": "code", @@ -125,8 +125,8 @@ }, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ " % Total % Received % Xferd Average Speed Time Time Time Current\n", " Dload Upload Total Spent Left Speed\n", @@ -194,8 +194,8 @@ }, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "nohup: appending output to 'nohup.out'\n" ] @@ -212,16 +212,16 @@ "cell_type": "code", "execution_count": 6, "metadata": { - "id": "4teHBr6W5_Mz", - "outputId": "977f6410-431b-4015-be80-f0278df7f058", "colab": { "base_uri": "https://localhost:8080/" - } + }, + "id": "4teHBr6W5_Mz", + "outputId": "977f6410-431b-4015-be80-f0278df7f058" }, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "Cloning into 'nada-ai'...\n", "remote: Enumerating objects: 1483, done.\u001b[K\n", @@ -273,8 +273,8 @@ }, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "Building program: \u001b[1m\u001b[32mlinear_regression\u001b[39m\u001b[0m\n", "\u001b[1;32mBuild complete!\u001b[0m\n" @@ -297,8 +297,8 @@ }, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "linear_regression.nada.bin\n" ] @@ -343,8 +343,8 @@ }, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "Running test: \u001b[1m\u001b[32mlinear_regression\u001b[39m\u001b[0m\n", "Building ...\n", @@ -390,8 +390,8 @@ }, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "Getting quote for operation...\n", "Submitting payment receipt 2 unil, tx hash C9B4C1614E43958E1174F3A8068CB8385B2A72EBD6224381CC0C4039C810D3DD\n", @@ -461,4 +461,4 @@ }, "nbformat": 4, "nbformat_minor": 0 -} \ No newline at end of file +} diff --git a/examples/neural_net/neural_net.ipynb b/examples/neural_net/neural_net.ipynb index efb1590..6214156 100644 --- a/examples/neural_net/neural_net.ipynb +++ b/examples/neural_net/neural_net.ipynb @@ -56,16 +56,16 @@ "cell_type": "code", "execution_count": 1, "metadata": { - "id": "o4PXBlE2v_7K", "colab": { "base_uri": "https://localhost:8080/" }, + "id": "o4PXBlE2v_7K", "outputId": "4b268ef1-60d7-49df-b9c1-51c4038addd1" }, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m61.0/61.0 kB\u001b[0m \u001b[31m1.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m54.8/54.8 kB\u001b[0m \u001b[31m2.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", @@ -84,7 +84,7 @@ } ], "source": [ - "%pip install nada-ai~=0.3.0 --quiet" + "%pip install nada-ai --quiet" ] }, { @@ -102,16 +102,16 @@ }, { "cell_type": "code", + "execution_count": 3, + "metadata": { + "id": "nQVtynG6_CSG" + }, + "outputs": [], "source": [ "# Configure telemetry settings\n", "enable_telemetry = True #@param {type:\"boolean\"}\n", "my_identifier = \"your-telemetry-identifier\" #@param {type:\"string\"}" - ], - "metadata": { - "id": "nQVtynG6_CSG" - }, - "execution_count": 3, - "outputs": [] + ] }, { "cell_type": "code", @@ -125,8 +125,8 @@ }, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ " % Total % Received % Xferd Average Speed Time Time Time Current\n", " Dload Upload Total Spent Left Speed\n", @@ -194,8 +194,8 @@ }, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "nohup: appending output to 'nohup.out'\n" ] @@ -212,16 +212,16 @@ "cell_type": "code", "execution_count": 6, "metadata": { - "id": "4teHBr6W5_Mz", "colab": { "base_uri": "https://localhost:8080/" }, + "id": "4teHBr6W5_Mz", "outputId": "44a9abf7-c3fd-4e46-ad8a-5ca9582fbef0" }, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "Cloning into 'nada-ai'...\n", "remote: Enumerating objects: 1483, done.\u001b[K\n", @@ -273,8 +273,8 @@ }, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "Building program: \u001b[1m\u001b[32mneural_net\u001b[39m\u001b[0m\n", "\u001b[1;32mBuild complete!\u001b[0m\n" @@ -297,8 +297,8 @@ }, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "neural_net.nada.bin\n" ] @@ -343,8 +343,8 @@ }, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "Running test: \u001b[1m\u001b[32mneural_net\u001b[39m\u001b[0m\n", "Building ...\n", @@ -390,8 +390,8 @@ }, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "Getting quote for operation...\n", "Submitting payment receipt 2 unil, tx hash BA58D92BBBC4286DCDFCB0F7D26456C1C3B86FA0EF25B02597EDD7A4D0C0F9C6\n", @@ -464,4 +464,4 @@ }, "nbformat": 4, "nbformat_minor": 0 -} \ No newline at end of file +} diff --git a/examples/spam_detection/spam_detection.ipynb b/examples/spam_detection/spam_detection.ipynb index 1b688e7..31b0490 100644 --- a/examples/spam_detection/spam_detection.ipynb +++ b/examples/spam_detection/spam_detection.ipynb @@ -64,8 +64,8 @@ }, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m61.0/61.0 kB\u001b[0m \u001b[31m998.5 kB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m54.8/54.8 kB\u001b[0m \u001b[31m2.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", @@ -84,7 +84,7 @@ } ], "source": [ - "%pip install nada-ai~=0.3.0 --quiet" + "%pip install nada-ai --quiet" ] }, { @@ -102,16 +102,16 @@ }, { "cell_type": "code", + "execution_count": 3, + "metadata": { + "id": "jtEZDic_ASKl" + }, + "outputs": [], "source": [ "# Configure telemetry settings\n", "enable_telemetry = True #@param {type:\"boolean\"}\n", "my_identifier = \"your-telemetry-identifier\" #@param {type:\"string\"}" - ], - "metadata": { - "id": "jtEZDic_ASKl" - }, - "execution_count": 3, - "outputs": [] + ] }, { "cell_type": "code", @@ -125,8 +125,8 @@ }, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ " % Total % Received % Xferd Average Speed Time Time Time Current\n", " Dload Upload Total Spent Left Speed\n", @@ -194,8 +194,8 @@ }, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "nohup: appending output to 'nohup.out'\n" ] @@ -220,8 +220,8 @@ }, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "Cloning into 'nada-ai'...\n", "remote: Enumerating objects: 1483, done.\u001b[K\n", @@ -273,8 +273,8 @@ }, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "Building program: \u001b[1m\u001b[32mspam_detection\u001b[39m\u001b[0m\n", "\u001b[1;32mBuild complete!\u001b[0m\n" @@ -297,8 +297,8 @@ }, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "spam_detection.nada.bin\n" ] @@ -343,8 +343,8 @@ }, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "Running test: \u001b[1m\u001b[32mspam_detection\u001b[39m\u001b[0m\n", "Building ...\n", @@ -402,22 +402,22 @@ "cell_type": "code", "execution_count": 10, "metadata": { - "id": "_-X12FEUL_H4", - "outputId": "eed0411a-fe5a-4bdd-bf6b-e99470ecd6ad", "colab": { "base_uri": "https://localhost:8080/" - } + }, + "id": "_-X12FEUL_H4", + "outputId": "eed0411a-fe5a-4bdd-bf6b-e99470ecd6ad" }, "outputs": [ { - "output_type": "execute_result", "data": { "text/plain": [ "True" ] }, + "execution_count": 10, "metadata": {}, - "execution_count": 10 + "output_type": "execute_result" } ], "source": [ @@ -474,25 +474,21 @@ "cell_type": "code", "execution_count": 12, "metadata": { - "id": "Hb6I05y2MKDz", - "outputId": "84c14d63-08e6-467c-dbb4-75ebac904798", "colab": { "base_uri": "https://localhost:8080/", "height": 206 - } + }, + "id": "Hb6I05y2MKDz", + "outputId": "84c14d63-08e6-467c-dbb4-75ebac904798" }, "outputs": [ { - "output_type": "execute_result", "data": { - "text/plain": [ - " label message\n", - "0 ham Go until jurong point, crazy.. Available only ...\n", - "1 ham Ok lar... Joking wif u oni...\n", - "2 spam Free entry in 2 a wkly comp to win FA Cup fina...\n", - "3 ham U dun say so early hor... U c already then say...\n", - "4 ham Nah I don't think he goes to usf, he lives aro..." - ], + "application/vnd.google.colaboratory.intrinsic+json": { + "summary": "{\n \"name\": \"df\",\n \"rows\": 5572,\n \"fields\": [\n {\n \"column\": \"label\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 2,\n \"samples\": [\n \"spam\",\n \"ham\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"message\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 5169,\n \"samples\": [\n \"K, makes sense, btw carlos is being difficult so you guys are gonna smoke while I go pick up the second batch and get gas\",\n \"URGENT! Your mobile No *********** WON a \\u00a32,000 Bonus Caller Prize on 02/06/03! This is the 2nd attempt to reach YOU! Call 09066362220 ASAP! BOX97N7QP, 150ppm\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}", + "type": "dataframe", + "variable_name": "df" + }, "text/html": [ "\n", "
\n", @@ -757,14 +753,18 @@ "
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LogisticRegression()
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], + "text/plain": [ + "LogisticRegression()" ] }, + "execution_count": 15, "metadata": {}, - "execution_count": 15 + "output_type": "execute_result" } ], "source": [ @@ -1260,16 +1260,16 @@ "cell_type": "code", "execution_count": 16, "metadata": { - "id": "Lu9BidsnMN92", - "outputId": "7b49e423-51b8-4f73-9662-ec4da5665ac6", "colab": { "base_uri": "https://localhost:8080/" - } + }, + "id": "Lu9BidsnMN92", + "outputId": "7b49e423-51b8-4f73-9662-ec4da5665ac6" }, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "Accuracy: 98.2053%\n" ] @@ -1289,16 +1289,16 @@ "cell_type": "code", "execution_count": 17, "metadata": { - "id": "S3EiqgPOMQCh", - "outputId": "e338e1a6-38ff-40d4-ceb9-3e2d786e0c93", "colab": { "base_uri": "https://localhost:8080/" - } + }, + "id": "S3EiqgPOMQCh", + "outputId": "e338e1a6-38ff-40d4-ceb9-3e2d786e0c93" }, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "Optimal regression coefficients are: (1, 500)\n", "Optimal bias is: (1,)\n" @@ -1314,22 +1314,22 @@ "cell_type": "code", "execution_count": 18, "metadata": { - "id": "uGaedw7NMRHZ", - "outputId": "20991856-8241-400a-9fe1-3da244ed1dec", "colab": { "base_uri": "https://localhost:8080/" - } + }, + "id": "uGaedw7NMRHZ", + "outputId": "20991856-8241-400a-9fe1-3da244ed1dec" }, "outputs": [ { - "output_type": "execute_result", "data": { "text/plain": [ "['model/classifier.joblib']" ] }, + "execution_count": 18, "metadata": {}, - "execution_count": 18 + "output_type": "execute_result" } ], "source": [ @@ -1341,16 +1341,16 @@ "cell_type": "code", "execution_count": 19, "metadata": { - "id": "4s24ZNZ3MRKQ", - "outputId": "31ffef53-c17c-4d0d-8b41-efe441dc8493", "colab": { "base_uri": "https://localhost:8080/" - } + }, + "id": "4s24ZNZ3MRKQ", + "outputId": "31ffef53-c17c-4d0d-8b41-efe441dc8493" }, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "Storing program...\n", "Getting quote for operation...\n", @@ -1433,16 +1433,16 @@ "cell_type": "code", "execution_count": 23, "metadata": { - "id": "DgmLvYOb656M", - "outputId": "02141e64-5714-4d66-ede6-2b44ac8179a2", "colab": { "base_uri": "https://localhost:8080/" - } + }, + "id": "DgmLvYOb656M", + "outputId": "02141e64-5714-4d66-ede6-2b44ac8179a2" }, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "Storing input data...\n", "Getting quote for operation...\n", @@ -1469,16 +1469,16 @@ "cell_type": "code", "execution_count": 24, "metadata": { - "id": "DB8mjasbaGYs", - "outputId": "8ff227b8-1eaa-470d-8739-4decf108d4b0", "colab": { "base_uri": "https://localhost:8080/" - } + }, + "id": "DB8mjasbaGYs", + "outputId": "8ff227b8-1eaa-470d-8739-4decf108d4b0" }, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "Logit in plain text: 2.4080795630742746\n", "Probability of spam in plain text: 91.744134%\n" @@ -1543,4 +1543,4 @@ }, "nbformat": 4, "nbformat_minor": 0 -} \ No newline at end of file +} diff --git a/examples/time_series/time_series.ipynb b/examples/time_series/time_series.ipynb index 36abdb6..a460765 100644 --- a/examples/time_series/time_series.ipynb +++ b/examples/time_series/time_series.ipynb @@ -56,16 +56,16 @@ "cell_type": "code", "execution_count": 1, "metadata": { - "id": "o4PXBlE2v_7K", "colab": { "base_uri": "https://localhost:8080/" }, + "id": "o4PXBlE2v_7K", "outputId": "036c73f6-22c0-4868-ee69-869b987ed74a" }, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m61.0/61.0 kB\u001b[0m \u001b[31m1.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m54.8/54.8 kB\u001b[0m \u001b[31m2.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", @@ -84,7 +84,7 @@ } ], "source": [ - "%pip install nada-ai~=0.3.0 --quiet" + "%pip install nada-ai --quiet" ] }, { @@ -102,16 +102,16 @@ }, { "cell_type": "code", + "execution_count": 3, + "metadata": { + "id": "JxZ3jfRYBlmE" + }, + "outputs": [], "source": [ "# Configure telemetry settings\n", "enable_telemetry = True #@param {type:\"boolean\"}\n", "my_identifier = \"your-telemetry-identifier\" #@param {type:\"string\"}" - ], - "metadata": { - "id": "JxZ3jfRYBlmE" - }, - "execution_count": 3, - "outputs": [] + ] }, { "cell_type": "code", @@ -125,8 +125,8 @@ }, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ " % Total % Received % Xferd Average Speed Time Time Time Current\n", " Dload Upload Total Spent Left Speed\n", @@ -194,8 +194,8 @@ }, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "nohup: appending output to 'nohup.out'\n" ] @@ -212,16 +212,16 @@ "cell_type": "code", "execution_count": 6, "metadata": { - "id": "4teHBr6W5_Mz", "colab": { "base_uri": "https://localhost:8080/" }, + "id": "4teHBr6W5_Mz", "outputId": "5b0684da-f21e-49f8-ee44-6ab2846f6cae" }, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "Cloning into 'nada-ai'...\n", "remote: Enumerating objects: 1483, done.\u001b[K\n", @@ -273,8 +273,8 @@ }, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "Building program: \u001b[1m\u001b[32mtime_series\u001b[39m\u001b[0m\n", "\u001b[1;32mBuild complete!\u001b[0m\n" @@ -297,8 +297,8 @@ }, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "time_series.nada.bin\n" ] @@ -343,8 +343,8 @@ }, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "Running test: \u001b[1m\u001b[32mtime_series\u001b[39m\u001b[0m\n", "Building ...\n", @@ -390,8 +390,8 @@ }, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "15:11:42 - cmdstanpy - INFO - Chain [1] start processing\n", "15:11:42 - cmdstanpy - INFO - Chain [1] done processing\n", @@ -472,4 +472,4 @@ }, "nbformat": 4, "nbformat_minor": 0 -} \ No newline at end of file +}