Adds CAI Methods

- CreateModel
- Train
- Get Results
- Predict

Models/ModelParameters.cs

@@ -0,0 +1,247 @@
+/*
+ * QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
+ * Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+*/
+
+using Newtonsoft.Json;
+using Newtonsoft.Json.Converters;
+using QuantConnect.Util;
+
+namespace QuantConnect.PredictNowNET.Models;
+
+/// <summary>
+/// Type of Analysis
+/// </summary>
+[JsonConverter(typeof(StringEnumConverter))]
+public enum Analysis
+{
+    /// <summary>
+    /// No analysis
+    /// </summary>
+    None,
+    /// <summary>
+    /// 
+    /// </summary>
+    Small
+}
+
+/// <summary>
+/// Type of Boost algorithms
+/// </summary>
+[JsonConverter(typeof(StringEnumConverter))]
+public enum Boost
+{
+    /// <summary>
+    /// Dropouts meet Multiple Additive Regression Trees.
+    /// </summary>
+    Dart,
+    /// <summary>
+    /// Gradient-boosted decision trees
+    /// </summary>
+    Gbdt
+}
+
+/// <summary>
+/// Feature selection
+/// </summary>
+[JsonConverter(typeof(StringEnumConverter))]
+public enum FeatureSelection
+{
+    /// <summary>
+    /// No selection
+    /// </summary>
+    None,
+    /// <summary>
+    /// SHapley Additive exPlanations
+    /// </summary>
+    Shap,
+    /// <summary>
+    /// Computer-Mediated Discourse Analysis
+    /// </summary>
+    CMDA
+}
+
+/// <summary>
+/// Model modes
+/// </summary>
+[JsonConverter(typeof(StringEnumConverter))]
+public enum Mode
+{
+    /// <summary>
+    /// Train
+    /// </summary>
+    Train,
+    /// <summary>
+    /// Live
+    /// </summary>
+    Live
+}
+
+/// <summary>
+/// Model type
+/// </summary>
+[JsonConverter(typeof(StringEnumConverter))]
+public enum ModelType
+{
+    /// <summary>
+    /// Classification
+    /// </summary>
+    Classification,
+    /// <summary>
+    /// Regression
+    /// </summary>
+    Regression
+}
+
+/// <summary>
+/// Represents parameters for both backtests and live prediction
+/// </summary>
+public class ModelParameters
+{
+    /// <summary>
+    /// True if use timeseries
+    /// </summary>
+    [JsonProperty(PropertyName = "timeseries")]
+    [JsonConverter(typeof(YesNoJsonConverter))]
+    public bool Timeseries { get; private set; }
+
+    /// <summary>
+    /// The type of the mode. For example regression or classification
+    /// </summary>
+    [JsonProperty(PropertyName = "type")]
+    public ModelType Type { get; private set; }
+
+    /// <summary>
+    /// The feature selection
+    /// </summary>
+    [JsonProperty(PropertyName = "feature_selection")]
+    public FeatureSelection FeatureSelection { get; private set; }
+
+    /// <summary>
+    /// The analysys output
+    /// </summary>
+    [JsonProperty(PropertyName = "analysis")]
+    public Analysis Analysis { get; private set; }
+
+    /// <summary>
+    /// Select the Boost algorithm
+    /// </summary>
+    [JsonProperty(PropertyName = "boost")]
+    public Boost Boost { get; private set; }
+
+    /// <summary>
+    /// The model mode
+    /// </summary>
+    [JsonProperty(PropertyName = "mode")]
+    public Mode Mode { get; }
+
+    /// <summary>
+    /// The size of the test sample. If less than 1 --> ratio, otherwise --> exact # of rows
+    /// </summary>
+    [JsonProperty(PropertyName = "testsize")]
+    public string Testsize { get; private set; }
+
+    /// <summary>
+    /// Define if should use weights: yes, no, or custom
+    /// </summary>
+    [JsonProperty(PropertyName = "weights")]
+    public string Weights { get; private set; }
+
+    /// <summary>
+    /// True if should refine the probability
+    /// </summary>
+    [JsonProperty(PropertyName = "prob_calib")]
+    [JsonConverter(typeof(YesNoJsonConverter))]
+    public bool ProbabilityCalibration { get; private set; }
+
+    /// <summary>
+    /// True if should use exploratory data analysis
+    /// </summary>
+    [JsonProperty(PropertyName = "eda")]
+    [JsonConverter(typeof(YesNoJsonConverter))]
+    public bool ExploratoryDataAnalysis { get; private set; }
+
+    /// <summary>
+    /// Random seed for initialization
+    /// </summary>
+    [JsonProperty(PropertyName = "random_seed")]
+    public string RandomSeed { get; private set; } = "1";
+
+    /// <summary>
+    /// Define custom weights
+    /// </summary>
+    [JsonProperty(PropertyName = "custom_weights")]
+    public string CustomWeights { get; private set; }
+
+    /// <summary>
+    /// Create a new instance of ModelParameters
+    /// </summary>
+    /// <param name="mode">The model mode</param>
+    /// <param name="type">The type of the mode. For example regression or classification</param>
+    /// <param name="featureSelection">The feature selection</param>
+    /// <param name="analysis">The analysys output</param>
+    /// <param name="boost">Select the Boost algorithm</param>
+    /// <param name="testsize">The size of the test sample. If less than 1 --> ratio, otherwise --> exact # of rows</param>
+    /// <param name="timeseries">True if use timeseries</param>
+    /// <param name="probabilityCalibration">True if should refine the probability</param>
+    /// <param name="exploratoryDataAnalysis">True if should use exploratory data analysis</param>
+    /// <param name="weights">Define if should use weights: yes, no, or custom</param>
+    /// <param name="customWeights">Define custom weights</param>
+    /// <param name="randomSeed">Random seed for initialization</param>
+    public ModelParameters(Mode mode, ModelType type, FeatureSelection featureSelection, Analysis analysis, Boost boost, double testsize, bool timeseries, bool probabilityCalibration, bool exploratoryDataAnalysis, string weights, string customWeights= "", double randomSeed = 1)
+    {
+        Mode = mode;
+        Type = type;
+        FeatureSelection = featureSelection;
+        Analysis = analysis;
+        Boost = boost;
+        Testsize = testsize.ToString();
+        Timeseries = timeseries;
+        ProbabilityCalibration = probabilityCalibration;
+        ExploratoryDataAnalysis = exploratoryDataAnalysis;
+        Weights = weights;
+        CustomWeights = customWeights;
+        RandomSeed = randomSeed.ToString();
+    }
+
+    /// <summary>
+    /// Returns a string that represents the current object
+    /// </summary>
+    /// <returns>A string that represents the current object</returns>
+    public override string ToString() => JsonConvert.SerializeObject(this);
+}
+
+/// <summary>
+/// Defines how bool should be serialized to json
+/// </summary>
+public class YesNoJsonConverter : TypeChangeJsonConverter<bool, string>
+{
+    /// <summary>
+    /// Convert the input value to a value to be serialized
+    /// </summary>
+    /// <param name="value">The input value to be converted before serialization</param>
+    /// <returns>A new instance of TResult that is to be serialized</returns>
+    protected override string Convert(bool value) => value ? "yes" : "no";
+
+    /// <summary>
+    /// Converts the input value to be deserialized
+    /// </summary>
+    /// <param name="value">The deserialized value that needs to be converted to T</param>
+    /// <returns>The converted value</returns>
+    protected override bool Convert(string value) => value.ToLower() switch
+    {
+        "yes" => true,
+        "no" => false,
+        _ => throw new ArgumentOutOfRangeException(nameof(value), $"Not expected value: {value}")
+    };
+}

Models/ModelResponse.cs

@@ -0,0 +1,48 @@
+/*
+ * QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
+ * Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+*/
+
+using Newtonsoft.Json;
+
+namespace QuantConnect.PredictNowNET.Models;
+
+/// <summary>
+/// Base model response
+/// </summary>
+public class ModelResponse
+{
+    /// <summary>
+    /// Model Name
+    /// </summary>
+    [JsonProperty(PropertyName = "model_name")]
+    public string ModelName { get; internal set; } = string.Empty;
+
+    /// <summary>
+    /// Result Message
+    /// </summary>
+    [JsonProperty(PropertyName = "message")]
+    public string Message { get; internal set; } = string.Empty;
+
+    /// <summary>
+    /// True if sucessfull 
+    /// </summary>
+    [JsonProperty(PropertyName = "success")]
+    public bool Success { get; internal set; }
+
+    /// <summary>
+    /// Returns a string that represents the current object
+    /// </summary>
+    /// <returns>A string that represents the current object</returns>
+    public override string ToString() => JsonConvert.SerializeObject(this);
+}

Models/PredictResult.cs

@@ -0,0 +1,87 @@
+/*
+ * QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
+ * Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+*/
+
+using Newtonsoft.Json;
+
+namespace QuantConnect.PredictNowNET.Models;
+
+/// <summary>
+/// Model Prediction Result
+/// </summary>
+public class PredictResult
+{
+    /// <summary>
+    /// True if should use exploratory data analysis
+    /// </summary>
+    [JsonProperty(PropertyName = "eda")]
+    public string ExploratoryDataAnalysis { get; internal set; } = string.Empty;
+
+    /// <summary>
+    /// Name of the file with the prediction
+    /// </summary>
+    [JsonProperty(PropertyName = "filename")]
+    public string Filename { get; internal set; } = string.Empty;
+
+    /// <summary>
+    /// Labels used in the prediction
+    /// </summary>
+    [JsonProperty(PropertyName = "labels")]
+    public string Labels { get; internal set; } = string.Empty;
+
+    /// <summary>
+    /// Objective function
+    /// </summary>
+    [JsonProperty(PropertyName = "objective")]
+    public string Objective { get; internal set; } = string.Empty;
+
+    /// <summary>
+    /// True if should refine the probability
+    /// </summary>
+    [JsonProperty(PropertyName = "prob_calib")]
+    public string ProbabilityCalibration { get; internal set; } = string.Empty;
+
+    /// <summary>
+    /// Probabilities
+    /// </summary>
+    [JsonProperty(PropertyName = "probabilities")]
+    public string Probabilities { get; internal set; } = string.Empty;
+
+    /// <summary>
+    /// Title
+    /// </summary>
+    [JsonProperty(PropertyName = "title")]
+    public string Title { get; internal set; } = string.Empty;
+
+    /// <summary>
+    /// 
+    /// </summary>
+    [JsonProperty(PropertyName = "too_many_nulls_list")]
+    public string TooManyNullsList { get; internal set; } = string.Empty;
+
+    /// <summary>
+    /// Result of the Prediction
+    /// </summary>
+    /// <param name="title"></param>
+    public PredictResult(string title)
+    {
+        Title = title;
+    }
+
+    /// <summary>
+    /// Returns a string that represents the current object
+    /// </summary>
+    /// <returns>A string that represents the current object</returns>
+    public override string ToString() => JsonConvert.SerializeObject(this);
+}